changeset 88:ae7c31133059 draft

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/marea_2/ras_to_bounds.py	Sat Jul 20 16:07:09 2024 +0000
@@ -0,0 +1,123 @@
+import argparse
+import utils.general_utils as utils
+from typing import Optional, List
+import os
+import numpy as np
+import pandas as pd
+import cobra
+from joblib import Parallel, delayed, cpu_count
+import sys
+
+################################# process args ###############################
+def process_args(args :List[str]) -> argparse.Namespace:
+    """
+    Processes command-line arguments.
+
+    Args:
+        args (list): List of command-line arguments.
+
+    Returns:
+        Namespace: An object containing parsed arguments.
+    """
+    parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
+                                     description = 'process some value\'s')
+    
+    parser.add_argument(
+        '-ms', '--model_selector', 
+        type = utils.Model, default = utils.Model.ENGRO2, choices = [utils.Model.ENGRO2, utils.Model.Custom],
+        help = 'chose which type of model you want use')
+    
+    parser.add_argument("-mo", "--model", type = str,
+        help = "path to input file with custom rules, if provided")
+    
+    parser.add_argument("-mn", "--model_name", type = str, help = "custom mode name")
+
+    parser.add_argument(
+        '-mes', '--medium_selector', 
+        default = "Open", choices = ["Open", "Custom"],
+        help = 'chose which type of medium you want use')
+    
+    parser.add_argument("-meo", "--medium", type = str,
+        help = "path to input file with custom medium, if provided")
+    
+    parser.add_argument("-men", "--medium_name", type = str, help = "custom medium name")
+
+    parser.add_argument('-ol', '--out_log', 
+                        help = "Output log")
+    
+    parser.add_argument('-td', '--tool_dir',
+                        type = str,
+                        required = True,
+                        help = 'your tool directory')
+    
+    parser.add_argument('-ir', '--input_ras',
+                        required = True,
+                        type=str,
+                        help = 'input ras')
+    
+    ARGS = parser.parse_args()
+    return ARGS
+
+########################### warning ###########################################
+def warning(s :str) -> None:
+    """
+    Log a warning message to an output log file and print it to the console.
+
+    Args:
+        s (str): The warning message to be logged and printed.
+    
+    Returns:
+      None
+    """
+    with open(ARGS.out_log, 'a') as log:
+        log.write(s + "\n\n")
+    print(s)
+
+
+def write_to_file(dataset: pd.DataFrame, name: str, keep_index:bool=False)->None:
+    dataset.to_csv(ARGS.output_folder + name + ".csv", sep = '\t', index = keep_index)
+
+
+
+############################# main ###########################################
+def main() -> None:
+    """
+    Initializes everything and sets the program in motion based on the fronted input arguments.
+
+    Returns:
+        None
+    """
+    if not os.path.exists('model_generator'):
+        os.makedirs('model_generator')
+
+    num_processors = cpu_count()
+
+    global ARGS
+    ARGS = process_args(sys.argv)
+
+    ARGS.output_folder = 'model_generator/'
+    
+    ARGS.output_types = ARGS.output_type.split(",")
+
+    ras = pd.read_table(ARGS.input_ras, header=0, sep=r'\s+', index_col = 0).T
+    ras.replace("None", None, inplace=True)
+    ras = ras.astype(float)
+
+    #medium has rows cells and columns medium reactions, not common reactions set to None
+    medium = pd.read_csv(ARGS.input_medium, sep = '\t', header = 0, engine='python', index_col = 0)
+    medium = ras.astype(float)
+
+    model_type :utils.Model = ARGS.model_selector
+    if model_type is utils.Model.Custom:
+        model = model_type.getCOBRAmodel(customPath = utils.FilePath.fromStrPath(ARGS.model), customExtension = utils.FilePath.fromStrPath(ARGS.model_name).ext)
+    else:
+        model = model_type.getCOBRAmodel(toolDir=ARGS.tool_dir)
+
+    '''for index, row in ras.iterrows(): #iterate over cells RAS
+        generate_model(model, index, row, medium.loc[index], ARGS.output_model_format)'''
+
+    pass
+        
+##############################################################################
+if __name__ == "__main__":
+    main()
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