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489
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1 """
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2 Flux sampling and analysis utilities for COBRA models.
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3
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4 This script supports two modes:
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5 - Mode 1 (model_and_bounds=True): load a base model and apply bounds from
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6 separate files before sampling.
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7 - Mode 2 (model_and_bounds=False): load complete models and sample directly.
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8
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9 Sampling algorithms supported: OPTGP and CBS. Outputs include flux samples
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10 and optional analyses (pFBA, FVA, sensitivity), saved as tabular files.
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11 """
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12
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4
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13 import argparse
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14 import utils.general_utils as utils
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489
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15 from typing import List
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4
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16 import os
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489
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17 import pandas as pd
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4
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18 import numpy as np
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19 import cobra
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20 import utils.CBS_backend as CBS_backend
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21 from joblib import Parallel, delayed, cpu_count
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22 from cobra.sampling import OptGPSampler
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23 import sys
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489
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24 import utils.model_utils as model_utils
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25
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4
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26
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27 ################################# process args ###############################
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489
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28 def process_args(args: List[str] = None) -> argparse.Namespace:
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4
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29 """
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30 Processes command-line arguments.
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489
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31
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4
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32 Args:
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33 args (list): List of command-line arguments.
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489
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34
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4
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35 Returns:
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36 Namespace: An object containing parsed arguments.
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37 """
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489
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38 parser = argparse.ArgumentParser(usage='%(prog)s [options]',
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39 description='process some value\'s')
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40
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41 parser.add_argument("-mo", "--model_upload", type=str,
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42 help="path to input file with custom rules, if provided")
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43
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44 parser.add_argument("-mab", "--model_and_bounds", type=str,
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45 choices=['True', 'False'],
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46 required=True,
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47 help="upload mode: True for model+bounds, False for complete models")
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48
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49 parser.add_argument("-ens", "--sampling_enabled", type=str,
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50 choices=['true', 'false'],
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51 required=True,
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52 help="enable sampling: 'true' for sampling, 'false' for no sampling")
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53
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54 parser.add_argument('-ol', '--out_log',
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55 help="Output log")
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4
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56
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57 parser.add_argument('-td', '--tool_dir',
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489
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58 type=str,
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59 required=True,
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60 help='your tool directory')
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4
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61
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62 parser.add_argument('-in', '--input',
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489
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63 required=True,
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64 type=str,
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489
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65 help='input bounds files or complete model files')
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4
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66
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489
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67 parser.add_argument('-ni', '--name',
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68 required=True,
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69 type=str,
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70 help='cell names')
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4
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71
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72 parser.add_argument('-a', '--algorithm',
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489
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73 type=str,
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74 choices=['OPTGP', 'CBS'],
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75 required=True,
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76 help='choose sampling algorithm')
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4
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77
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489
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78 parser.add_argument('-th', '--thinning',
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79 type=int,
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80 default=100,
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81 required=True,
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82 help='choose thinning')
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4
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83
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489
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84 parser.add_argument('-ns', '--n_samples',
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85 type=int,
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86 required=True,
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87 help='choose how many samples (set to 0 for optimization only)')
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88
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89 parser.add_argument('-sd', '--seed',
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90 type=int,
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91 required=True,
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92 help='seed for random number generation')
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4
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93
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489
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94 parser.add_argument('-nb', '--n_batches',
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95 type=int,
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96 required=True,
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97 help='choose how many batches')
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4
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98
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489
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99 parser.add_argument('-opt', '--perc_opt',
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100 type=float,
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101 default=0.9,
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102 required=False,
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103 help='choose the fraction of optimality for FVA (0-1)')
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4
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104
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489
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105 parser.add_argument('-ot', '--output_type',
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106 type=str,
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107 required=True,
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108 help='output type for sampling results')
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4
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109
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489
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110 parser.add_argument('-ota', '--output_type_analysis',
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111 type=str,
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112 required=False,
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113 help='output type analysis (optimization methods)')
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515
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114
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489
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115 parser.add_argument('-idop', '--output_path',
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116 type=str,
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515
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117 default='flux_simulation/',
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118 help = 'output path for fluxes')
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119
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120 parser.add_argument('-otm', '--out_mean',
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121 type = str,
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517
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122 required=False,
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515
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123 help = 'output of mean of fluxes')
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147
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124
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515
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125 parser.add_argument('-otmd', '--out_median',
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126 type = str,
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517
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127 required=False,
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515
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128 help = 'output of median of fluxes')
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129
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130 parser.add_argument('-otq', '--out_quantiles',
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131 type = str,
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517
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132 required=False,
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515
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133 help = 'output of quantiles of fluxes')
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134
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135 parser.add_argument('-otfva', '--out_fva',
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136 type = str,
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517
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137 required=False,
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515
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138 help = 'output of FVA results')
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139 parser.add_argument('-otp', '--out_pfba',
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140 type = str,
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517
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141 required=False,
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515
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142 help = 'output of pFBA results')
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143 parser.add_argument('-ots', '--out_sensitivity',
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144 type = str,
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517
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145 required=False,
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515
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146 help = 'output of sensitivity results')
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147
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147 ARGS = parser.parse_args(args)
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4
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148 return ARGS
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149 ########################### warning ###########################################
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150 def warning(s :str) -> None:
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151 """
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152 Log a warning message to an output log file and print it to the console.
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153
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154 Args:
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155 s (str): The warning message to be logged and printed.
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156
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157 Returns:
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158 None
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159 """
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160 with open(ARGS.out_log, 'a') as log:
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161 log.write(s + "\n\n")
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162 print(s)
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163
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164
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519
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165 def write_to_file(dataset: pd.DataFrame, path: str, keep_index:bool=False, name:str=None)->None:
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489
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166 """
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515
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167 Write a DataFrame to a TSV file under path with a given base name.
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489
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168
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169 Args:
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170 dataset: The DataFrame to write.
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519
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171 name: Base file name (without extension). If None, 'path' is treated as the full file path.
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515
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172 path: Directory path where the file will be saved.
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489
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173 keep_index: Whether to keep the DataFrame index in the file.
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174
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175 Returns:
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176 None
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177 """
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4
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178 dataset.index.name = 'Reactions'
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519
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179 if name:
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180 dataset.to_csv(os.path.join(path, name + ".csv"), sep = '\t', index = keep_index)
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181 else:
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182 dataset.to_csv(path, sep = '\t', index = keep_index)
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4
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183
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184 ############################ dataset input ####################################
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185 def read_dataset(data :str, name :str) -> pd.DataFrame:
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186 """
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187 Read a dataset from a CSV file and return it as a pandas DataFrame.
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188
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189 Args:
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190 data (str): Path to the CSV file containing the dataset.
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191 name (str): Name of the dataset, used in error messages.
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192
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193 Returns:
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194 pandas.DataFrame: DataFrame containing the dataset.
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195
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196 Raises:
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197 pd.errors.EmptyDataError: If the CSV file is empty.
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198 sys.exit: If the CSV file has the wrong format, the execution is aborted.
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199 """
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200 try:
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201 dataset = pd.read_csv(data, sep = '\t', header = 0, index_col=0, engine='python')
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202 except pd.errors.EmptyDataError:
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203 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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204 if len(dataset.columns) < 2:
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205 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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206 return dataset
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207
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208
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209
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489
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210 def OPTGP_sampler(model: cobra.Model, model_name: str, n_samples: int = 1000, thinning: int = 100, n_batches: int = 1, seed: int = 0) -> None:
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4
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211 """
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212 Samples from the OPTGP (Optimal Global Perturbation) algorithm and saves the results to CSV files.
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489
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213
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4
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214 Args:
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215 model (cobra.Model): The COBRA model to sample from.
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216 model_name (str): The name of the model, used in naming output files.
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217 n_samples (int, optional): Number of samples per batch. Default is 1000.
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218 thinning (int, optional): Thinning parameter for the sampler. Default is 100.
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219 n_batches (int, optional): Number of batches to run. Default is 1.
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220 seed (int, optional): Random seed for reproducibility. Default is 0.
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489
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221
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4
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222 Returns:
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223 None
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224 """
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489
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225 import numpy as np
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226
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227 # Get reaction IDs for consistent column ordering
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228 reaction_ids = [rxn.id for rxn in model.reactions]
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229
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230 # Sample and save each batch as numpy file
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231 for i in range(n_batches):
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4
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232 optgp = OptGPSampler(model, thinning, seed)
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233 samples = optgp.sample(n_samples)
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489
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234
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235 # Save as numpy array (more memory efficient)
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236 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_OPTGP.npy"
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237 np.save(batch_filename, samples.to_numpy())
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238
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239 seed += 1
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240
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241 # Merge all batches into a single DataFrame
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242 all_samples = []
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243
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244 for i in range(n_batches):
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245 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_OPTGP.npy"
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246 batch_data = np.load(batch_filename, allow_pickle=True)
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247 all_samples.append(batch_data)
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248
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249 # Concatenate all batches
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250 samplesTotal_array = np.vstack(all_samples)
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251
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252 # Convert back to DataFrame with proper column names
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253 samplesTotal = pd.DataFrame(samplesTotal_array, columns=reaction_ids)
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254
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255 # Save the final merged result as CSV
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519
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256 write_to_file(samplesTotal.T, ARGS.output_path, True, name=model_name)
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489
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257
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258 # Clean up temporary numpy files
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259 for i in range(n_batches):
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260 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_OPTGP.npy"
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261 if os.path.exists(batch_filename):
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262 os.remove(batch_filename)
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4
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263
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264
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489
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265 def CBS_sampler(model: cobra.Model, model_name: str, n_samples: int = 1000, n_batches: int = 1, seed: int = 0) -> None:
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4
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266 """
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267 Samples using the CBS (Constraint-based Sampling) algorithm and saves the results to CSV files.
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489
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268
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4
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269 Args:
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270 model (cobra.Model): The COBRA model to sample from.
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271 model_name (str): The name of the model, used in naming output files.
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272 n_samples (int, optional): Number of samples per batch. Default is 1000.
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273 n_batches (int, optional): Number of batches to run. Default is 1.
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274 seed (int, optional): Random seed for reproducibility. Default is 0.
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489
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275
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4
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276 Returns:
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277 None
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278 """
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489
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279 import numpy as np
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280
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281 # Get reaction IDs for consistent column ordering
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282 reaction_ids = [reaction.id for reaction in model.reactions]
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283
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284 # Perform FVA analysis once for all batches
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285 df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0).round(6)
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286
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287 # Generate random objective functions for all samples across all batches
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288 df_coefficients = CBS_backend.randomObjectiveFunction(model, n_samples * n_batches, df_FVA, seed=seed)
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4
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289
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489
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290 # Sample and save each batch as numpy file
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291 for i in range(n_batches):
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292 samples = pd.DataFrame(columns=reaction_ids, index=range(n_samples))
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293
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4
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294 try:
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489
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295 CBS_backend.randomObjectiveFunctionSampling(
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296 model,
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297 n_samples,
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298 df_coefficients.iloc[:, i * n_samples:(i + 1) * n_samples],
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299 samples
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300 )
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4
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301 except Exception as e:
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302 utils.logWarning(
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489
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303 f"Warning: GLPK solver has failed for {model_name}. Trying with COBRA interface. Error: {str(e)}",
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304 ARGS.out_log
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305 )
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306 CBS_backend.randomObjectiveFunctionSampling_cobrapy(
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307 model,
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308 n_samples,
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309 df_coefficients.iloc[:, i * n_samples:(i + 1) * n_samples],
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310 samples
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311 )
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312
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313 # Save as numpy array (more memory efficient)
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314 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_CBS.npy"
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315 utils.logWarning(batch_filename, ARGS.out_log)
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316 np.save(batch_filename, samples.to_numpy())
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317
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318 # Merge all batches into a single DataFrame
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319 all_samples = []
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320
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321 for i in range(n_batches):
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322 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_CBS.npy"
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323 batch_data = np.load(batch_filename, allow_pickle=True)
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324 all_samples.append(batch_data)
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325
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326 # Concatenate all batches
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327 samplesTotal_array = np.vstack(all_samples)
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328
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329 # Convert back to DataFrame with proper column namesq
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330 samplesTotal = pd.DataFrame(samplesTotal_array, columns=reaction_ids)
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331
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332 # Save the final merged result as CSV
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519
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333 write_to_file(samplesTotal.T, ARGS.output_path, True, name=model_name)
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489
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334
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335 # Clean up temporary numpy files
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336 for i in range(n_batches):
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337 batch_filename = f"{ARGS.output_path}/{model_name}_{i}_CBS.npy"
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338 if os.path.exists(batch_filename):
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339 os.remove(batch_filename)
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4
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340
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341
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489
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342
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343 def model_sampler_with_bounds(model_input_original: cobra.Model, bounds_path: str, cell_name: str) -> List[pd.DataFrame]:
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4
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344 """
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489
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345 MODE 1: Prepares the model with bounds from separate bounds file and performs sampling.
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4
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346
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347 Args:
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348 model_input_original (cobra.Model): The original COBRA model.
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349 bounds_path (str): Path to the CSV file containing the bounds dataset.
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350 cell_name (str): Name of the cell, used to generate filenames for output.
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351
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352 Returns:
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353 List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results.
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354 """
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355
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356 model_input = model_input_original.copy()
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357 bounds_df = read_dataset(bounds_path, "bounds dataset")
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358
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489
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359 # Apply bounds to model
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360 for rxn_index, row in bounds_df.iterrows():
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361 try:
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362 model_input.reactions.get_by_id(rxn_index).lower_bound = row.lower_bound
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363 model_input.reactions.get_by_id(rxn_index).upper_bound = row.upper_bound
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364 except KeyError:
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365 warning(f"Warning: Reaction {rxn_index} not found in model. Skipping.")
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4
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366
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489
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367 return perform_sampling_and_analysis(model_input, cell_name)
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368
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4
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369
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489
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370 def perform_sampling_and_analysis(model_input: cobra.Model, cell_name: str) -> List[pd.DataFrame]:
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371 """
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372 Common function to perform sampling and analysis on a prepared model.
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4
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373
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489
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374 Args:
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375 model_input (cobra.Model): The prepared COBRA model with bounds applied.
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376 cell_name (str): Name of the cell, used to generate filenames for output.
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4
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377
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489
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378 Returns:
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379 List[pd.DataFrame]: A list of DataFrames containing statistics and analysis results.
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380 """
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|
4
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381
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382 returnList = []
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489
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383
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|
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384 if ARGS.sampling_enabled == "true":
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385
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386 if ARGS.algorithm == 'OPTGP':
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387 OPTGP_sampler(model_input, cell_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed)
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388 elif ARGS.algorithm == 'CBS':
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389 CBS_sampler(model_input, cell_name, ARGS.n_samples, ARGS.n_batches, ARGS.seed)
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390
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391 df_mean, df_median, df_quantiles = fluxes_statistics(cell_name, ARGS.output_types)
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392
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393 if("fluxes" not in ARGS.output_types):
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394 os.remove(ARGS.output_path + "/" + cell_name + '.csv')
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395
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396 returnList = [df_mean, df_median, df_quantiles]
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4
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397
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210
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398 df_pFBA, df_FVA, df_sensitivity = fluxes_analysis(model_input, cell_name, ARGS.output_type_analysis)
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4
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399
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400 if("pFBA" in ARGS.output_type_analysis):
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401 returnList.append(df_pFBA)
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402 if("FVA" in ARGS.output_type_analysis):
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403 returnList.append(df_FVA)
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404 if("sensitivity" in ARGS.output_type_analysis):
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405 returnList.append(df_sensitivity)
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406
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407 return returnList
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408
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409 def fluxes_statistics(model_name: str, output_types:List)-> List[pd.DataFrame]:
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410 """
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411 Computes statistics (mean, median, quantiles) for the fluxes.
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412
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413 Args:
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414 model_name (str): Name of the model, used in filename for input.
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415 output_types (List[str]): Types of statistics to compute (mean, median, quantiles).
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416
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417 Returns:
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418 List[pd.DataFrame]: List of DataFrames containing mean, median, and quantiles statistics.
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|
|
419 """
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420
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421 df_mean = pd.DataFrame()
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422 df_median= pd.DataFrame()
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423 df_quantiles= pd.DataFrame()
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424
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161
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425 df_samples = pd.read_csv(ARGS.output_path + "/" + model_name + '.csv', sep = '\t', index_col = 0).T
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4
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426 df_samples = df_samples.round(8)
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427
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428 for output_type in output_types:
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429 if(output_type == "mean"):
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430 df_mean = df_samples.mean()
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431 df_mean = df_mean.to_frame().T
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432 df_mean = df_mean.reset_index(drop=True)
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433 df_mean.index = [model_name]
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434 elif(output_type == "median"):
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435 df_median = df_samples.median()
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436 df_median = df_median.to_frame().T
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437 df_median = df_median.reset_index(drop=True)
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438 df_median.index = [model_name]
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439 elif(output_type == "quantiles"):
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440 newRow = []
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441 cols = []
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|
442 for rxn in df_samples.columns:
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443 quantiles = df_samples[rxn].quantile([0.25, 0.50, 0.75])
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|
|
444 newRow.append(quantiles[0.25])
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|
|
445 cols.append(rxn + "_q1")
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446 newRow.append(quantiles[0.5])
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|
|
447 cols.append(rxn + "_q2")
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|
|
448 newRow.append(quantiles[0.75])
|
|
|
449 cols.append(rxn + "_q3")
|
|
|
450 df_quantiles = pd.DataFrame(columns=cols)
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|
|
451 df_quantiles.loc[0] = newRow
|
|
|
452 df_quantiles = df_quantiles.reset_index(drop=True)
|
|
|
453 df_quantiles.index = [model_name]
|
|
|
454
|
|
|
455 return df_mean, df_median, df_quantiles
|
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|
456
|
|
|
457 def fluxes_analysis(model:cobra.Model, model_name:str, output_types:List)-> List[pd.DataFrame]:
|
|
|
458 """
|
|
489
|
459 Performs flux analysis including pFBA, FVA, and sensitivity analysis. The objective function
|
|
|
460 is assumed to be already set in the model.
|
|
4
|
461
|
|
|
462 Args:
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|
|
463 model (cobra.Model): The COBRA model to analyze.
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|
|
464 model_name (str): Name of the model, used in filenames for output.
|
|
|
465 output_types (List[str]): Types of analysis to perform (pFBA, FVA, sensitivity).
|
|
|
466
|
|
|
467 Returns:
|
|
|
468 List[pd.DataFrame]: List of DataFrames containing pFBA, FVA, and sensitivity analysis results.
|
|
|
469 """
|
|
|
470
|
|
|
471 df_pFBA = pd.DataFrame()
|
|
|
472 df_FVA= pd.DataFrame()
|
|
|
473 df_sensitivity= pd.DataFrame()
|
|
|
474
|
|
|
475 for output_type in output_types:
|
|
|
476 if(output_type == "pFBA"):
|
|
|
477 solution = cobra.flux_analysis.pfba(model)
|
|
|
478 fluxes = solution.fluxes
|
|
489
|
479 df_pFBA.loc[0,[rxn.id for rxn in model.reactions]] = fluxes.tolist()
|
|
4
|
480 df_pFBA = df_pFBA.reset_index(drop=True)
|
|
|
481 df_pFBA.index = [model_name]
|
|
|
482 df_pFBA = df_pFBA.astype(float).round(6)
|
|
|
483 elif(output_type == "FVA"):
|
|
489
|
484 fva = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=ARGS.perc_opt, processes=1).round(8)
|
|
4
|
485 columns = []
|
|
|
486 for rxn in fva.index.to_list():
|
|
|
487 columns.append(rxn + "_min")
|
|
|
488 columns.append(rxn + "_max")
|
|
|
489 df_FVA= pd.DataFrame(columns = columns)
|
|
|
490 for index_rxn, row in fva.iterrows():
|
|
|
491 df_FVA.loc[0, index_rxn+ "_min"] = fva.loc[index_rxn, "minimum"]
|
|
|
492 df_FVA.loc[0, index_rxn+ "_max"] = fva.loc[index_rxn, "maximum"]
|
|
|
493 df_FVA = df_FVA.reset_index(drop=True)
|
|
|
494 df_FVA.index = [model_name]
|
|
|
495 df_FVA = df_FVA.astype(float).round(6)
|
|
|
496 elif(output_type == "sensitivity"):
|
|
|
497 solution_original = model.optimize().objective_value
|
|
|
498 reactions = model.reactions
|
|
|
499 single = cobra.flux_analysis.single_reaction_deletion(model)
|
|
|
500 newRow = []
|
|
|
501 df_sensitivity = pd.DataFrame(columns = [rxn.id for rxn in reactions], index = [model_name])
|
|
|
502 for rxn in reactions:
|
|
|
503 newRow.append(single.knockout[rxn.id].growth.values[0]/solution_original)
|
|
|
504 df_sensitivity.loc[model_name] = newRow
|
|
|
505 df_sensitivity = df_sensitivity.astype(float).round(6)
|
|
|
506 return df_pFBA, df_FVA, df_sensitivity
|
|
|
507
|
|
|
508 ############################# main ###########################################
|
|
489
|
509 def main(args: List[str] = None) -> None:
|
|
4
|
510 """
|
|
489
|
511 Initialize and run sampling/analysis based on the frontend input arguments.
|
|
4
|
512
|
|
|
513 Returns:
|
|
|
514 None
|
|
|
515 """
|
|
|
516
|
|
489
|
517 num_processors = max(1, cpu_count() - 1)
|
|
4
|
518
|
|
|
519 global ARGS
|
|
147
|
520 ARGS = process_args(args)
|
|
158
|
521
|
|
515
|
522 if not os.path.exists('flux_simulation'):
|
|
|
523 os.makedirs('flux_simulation')
|
|
489
|
524
|
|
|
525 # --- Normalize inputs (the tool may pass comma-separated --input and either --name or --names) ---
|
|
|
526 ARGS.input_files = ARGS.input.split(",") if ARGS.input else []
|
|
|
527 ARGS.file_names = ARGS.name.split(",")
|
|
|
528 # output types (required) -> list
|
|
|
529 ARGS.output_types = ARGS.output_type.split(",") if ARGS.output_type else []
|
|
|
530 # optional analysis output types -> list or empty
|
|
|
531 ARGS.output_type_analysis = ARGS.output_type_analysis.split(",") if ARGS.output_type_analysis else []
|
|
|
532
|
|
|
533 # Determine if sampling should be performed
|
|
|
534 if ARGS.sampling_enabled == "true":
|
|
|
535 perform_sampling = True
|
|
4
|
536 else:
|
|
489
|
537 perform_sampling = False
|
|
334
|
538
|
|
489
|
539 print("=== INPUT FILES ===")
|
|
|
540 print(f"{ARGS.input_files}")
|
|
|
541 print(f"{ARGS.file_names}")
|
|
|
542 print(f"{ARGS.output_type}")
|
|
|
543 print(f"{ARGS.output_types}")
|
|
|
544 print(f"{ARGS.output_type_analysis}")
|
|
|
545 print(f"Sampling enabled: {perform_sampling} (n_samples: {ARGS.n_samples})")
|
|
4
|
546
|
|
489
|
547 if ARGS.model_and_bounds == "True":
|
|
|
548 # MODE 1: Model + bounds (separate files)
|
|
|
549 print("=== MODE 1: Model + Bounds (separate files) ===")
|
|
|
550
|
|
|
551 # Load base model
|
|
|
552 if not ARGS.model_upload:
|
|
|
553 sys.exit("Error: model_upload is required for Mode 1")
|
|
4
|
554
|
|
489
|
555 base_model = model_utils.build_cobra_model_from_csv(ARGS.model_upload)
|
|
4
|
556
|
|
489
|
557 validation = model_utils.validate_model(base_model)
|
|
|
558
|
|
|
559 print("\n=== MODEL VALIDATION ===")
|
|
|
560 for key, value in validation.items():
|
|
|
561 print(f"{key}: {value}")
|
|
|
562
|
|
|
563 # Set solver verbosity to 1 to see warning and error messages only.
|
|
|
564 base_model.solver.configuration.verbosity = 1
|
|
4
|
565
|
|
489
|
566 # Process each bounds file with the base model
|
|
|
567 results = Parallel(n_jobs=num_processors)(
|
|
|
568 delayed(model_sampler_with_bounds)(base_model, bounds_file, cell_name)
|
|
|
569 for bounds_file, cell_name in zip(ARGS.input_files, ARGS.file_names)
|
|
|
570 )
|
|
4
|
571
|
|
489
|
572 else:
|
|
|
573 # MODE 2: Multiple complete models
|
|
|
574 print("=== MODE 2: Multiple complete models ===")
|
|
|
575
|
|
|
576 # Process each complete model file
|
|
|
577 results = Parallel(n_jobs=num_processors)(
|
|
|
578 delayed(perform_sampling_and_analysis)(model_utils.build_cobra_model_from_csv(model_file), cell_name)
|
|
|
579 for model_file, cell_name in zip(ARGS.input_files, ARGS.file_names)
|
|
|
580 )
|
|
|
581
|
|
|
582 # Handle sampling outputs (only if sampling was performed)
|
|
|
583 if perform_sampling:
|
|
|
584 print("=== PROCESSING SAMPLING RESULTS ===")
|
|
|
585
|
|
|
586 all_mean = pd.concat([result[0] for result in results], ignore_index=False)
|
|
|
587 all_median = pd.concat([result[1] for result in results], ignore_index=False)
|
|
|
588 all_quantiles = pd.concat([result[2] for result in results], ignore_index=False)
|
|
4
|
589
|
|
489
|
590 if "mean" in ARGS.output_types:
|
|
|
591 all_mean = all_mean.fillna(0.0)
|
|
|
592 all_mean = all_mean.sort_index()
|
|
519
|
593 write_to_file(all_mean.T, ARGS.out_mean, True)
|
|
489
|
594
|
|
|
595 if "median" in ARGS.output_types:
|
|
|
596 all_median = all_median.fillna(0.0)
|
|
|
597 all_median = all_median.sort_index()
|
|
519
|
598 write_to_file(all_median.T, ARGS.out_median, True)
|
|
489
|
599
|
|
|
600 if "quantiles" in ARGS.output_types:
|
|
|
601 all_quantiles = all_quantiles.fillna(0.0)
|
|
|
602 all_quantiles = all_quantiles.sort_index()
|
|
519
|
603 write_to_file(all_quantiles.T, ARGS.out_quantiles, True)
|
|
489
|
604 else:
|
|
|
605 print("=== SAMPLING SKIPPED (n_samples = 0 or sampling disabled) ===")
|
|
|
606
|
|
|
607 # Handle optimization analysis outputs (always available)
|
|
|
608 print("=== PROCESSING OPTIMIZATION RESULTS ===")
|
|
4
|
609
|
|
489
|
610 # Determine the starting index for optimization results
|
|
|
611 # If sampling was performed, optimization results start at index 3
|
|
|
612 # If no sampling, optimization results start at index 0
|
|
|
613 index_result = 3 if perform_sampling else 0
|
|
|
614
|
|
|
615 if "pFBA" in ARGS.output_type_analysis:
|
|
4
|
616 all_pFBA = pd.concat([result[index_result] for result in results], ignore_index=False)
|
|
|
617 all_pFBA = all_pFBA.sort_index()
|
|
519
|
618 write_to_file(all_pFBA.T, ARGS.out_pfba, True)
|
|
489
|
619 index_result += 1
|
|
|
620
|
|
|
621 if "FVA" in ARGS.output_type_analysis:
|
|
|
622 all_FVA = pd.concat([result[index_result] for result in results], ignore_index=False)
|
|
4
|
623 all_FVA = all_FVA.sort_index()
|
|
519
|
624 write_to_file(all_FVA.T, ARGS.out_fva, True)
|
|
489
|
625 index_result += 1
|
|
|
626
|
|
|
627 if "sensitivity" in ARGS.output_type_analysis:
|
|
4
|
628 all_sensitivity = pd.concat([result[index_result] for result in results], ignore_index=False)
|
|
|
629 all_sensitivity = all_sensitivity.sort_index()
|
|
519
|
630 write_to_file(all_sensitivity.T, ARGS.out_sensitivity, True)
|
|
4
|
631
|
|
489
|
632 return
|
|
4
|
633
|
|
|
634 ##############################################################################
|
|
|
635 if __name__ == "__main__":
|
|
|
636 main() |
