--- a/COBRAxy/ras_to_bounds.py	Sun Oct 13 11:33:51 2024 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,276 +0,0 @@
-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
-import sys
-import csv
-from joblib import Parallel, delayed, cpu_count
-
-################################# 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 = "allOpen",
-        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('-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',
-                        type=str,
-                        required = False,
-                        help = 'input ras')
-    
-    parser.add_argument('-rn', '--names',
-                type=str,
-                help = 'ras class names')
-    
-    parser.add_argument('-rs', '--ras_selector',
-                        required = True,
-                        type=utils.Bool("using_RAS"),
-                        help = 'ras selector')
-
-    parser.add_argument('-cc', '--cell_class',
-                    type = str,
-                    help = 'output of cell class')
-    
-    
-    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)
-
-############################ dataset input ####################################
-def read_dataset(data :str, name :str) -> pd.DataFrame:
-    """
-    Read a dataset from a CSV file and return it as a pandas DataFrame.
-
-    Args:
-        data (str): Path to the CSV file containing the dataset.
-        name (str): Name of the dataset, used in error messages.
-
-    Returns:
-        pandas.DataFrame: DataFrame containing the dataset.
-
-    Raises:
-        pd.errors.EmptyDataError: If the CSV file is empty.
-        sys.exit: If the CSV file has the wrong format, the execution is aborted.
-    """
-    try:
-        dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
-    except pd.errors.EmptyDataError:
-        sys.exit('Execution aborted: wrong format of ' + name + '\n')
-    if len(dataset.columns) < 2:
-        sys.exit('Execution aborted: wrong format of ' + name + '\n')
-    return dataset
-
-
-def apply_ras_bounds(model, ras_row, rxns_ids):
-    """
-    Adjust the bounds of reactions in the model based on RAS values.
-
-    Args:
-        model (cobra.Model): The metabolic model to be modified.
-        ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
-        rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
-    
-    Returns:
-        None
-    """
-    for reaction in rxns_ids:
-        if reaction in ras_row.index:
-            scaling_factor = ras_row[reaction]
-            lower_bound=model.reactions.get_by_id(reaction).lower_bound
-            upper_bound=model.reactions.get_by_id(reaction).upper_bound
-            #warning("Reaction: "+reaction+" Lower Bound: "+str(lower_bound)+" Upper Bound: "+str(upper_bound)+" Scaling Factor: "+str(scaling_factor))
-            valMax=float((upper_bound)*scaling_factor)
-            valMin=float((lower_bound)*scaling_factor)
-            if upper_bound!=0 and lower_bound==0:
-                model.reactions.get_by_id(reaction).upper_bound=valMax
-            if upper_bound==0 and lower_bound!=0:
-                model.reactions.get_by_id(reaction).lower_bound=valMin
-            if upper_bound!=0 and lower_bound!=0:
-                model.reactions.get_by_id(reaction).lower_bound=valMin
-                model.reactions.get_by_id(reaction).upper_bound=valMax
-    pass
-
-def process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder):
-    """
-    Process a single RAS cell, apply bounds, and save the bounds to a CSV file.
-
-    Args:
-        cellName (str): The name of the RAS cell (used for naming the output file).
-        ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
-        model (cobra.Model): The metabolic model to be modified.
-        rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
-        output_folder (str): Folder path where the output CSV file will be saved.
-    
-    Returns:
-        None
-    """
-    model_new = model.copy()
-    apply_ras_bounds(model_new, ras_row, rxns_ids)
-    bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
-    bounds.to_csv(output_folder + cellName + ".csv", sep='\t', index=True)
-    pass
-
-def generate_bounds(model: cobra.Model, medium: dict, ras=None, output_folder='output/') -> pd.DataFrame:
-    """
-    Generate reaction bounds for a metabolic model based on medium conditions and optional RAS adjustments.
-    
-    Args:
-        model (cobra.Model): The metabolic model for which bounds will be generated.
-        medium (dict): A dictionary where keys are reaction IDs and values are the medium conditions.
-        ras (pd.DataFrame, optional): RAS pandas dataframe. Defaults to None.
-        output_folder (str, optional): Folder path where output CSV files will be saved. Defaults to 'output/'.
-
-    Returns:
-        pd.DataFrame: DataFrame containing the bounds of reactions in the model.
-    """
-    rxns_ids = [rxn.id for rxn in model.reactions]
-    
-    # Set medium conditions
-    for reaction, value in medium.items():
-        if value is not None:
-            model.reactions.get_by_id(reaction).lower_bound = -float(value)
-    
-    # Perform Flux Variability Analysis (FVA)
-    df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
-    
-    # Set FVA bounds
-    for reaction in rxns_ids:
-        rxn = model.reactions.get_by_id(reaction)
-        rxn.lower_bound = float(df_FVA.loc[reaction, "minimum"])
-        rxn.upper_bound = float(df_FVA.loc[reaction, "maximum"])
-
-    if ras is not None:
-        Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows())
-         #for cellName, ras_row in ras.iterrows():
-            #process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder) 
-    else:
-        model_new = model.copy()
-        apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids)
-        bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
-        bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)
-    pass
-
-
-
-############################# 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('ras_to_bounds'):
-        os.makedirs('ras_to_bounds')
-
-
-    global ARGS
-    ARGS = process_args(sys.argv)
-
-    ARGS.output_folder = 'ras_to_bounds/'
-
-    if(ARGS.ras_selector == True):
-        ras_file_list = ARGS.input_ras.split(",")
-        ras_file_names = ARGS.names.split(",")
-        ras_class_names = []
-        for file in ras_file_names:
-            ras_class_names.append(file.split(".")[0])
-        ras_list = []
-        class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"])
-        for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names):
-            ras = read_dataset(ras_matrix, "ras dataset")
-            ras.replace("None", None, inplace=True)
-            ras.set_index("Reactions", drop=True, inplace=True)
-            ras = ras.T
-            ras = ras.astype(float)
-            ras_list.append(ras)
-            for patient_id in ras.index:
-                class_assignments.loc[class_assignments.shape[0]] = [patient_id, ras_class_name]
-        
-        
-        # Concatenate all ras DataFrames into a single DataFrame
-        ras_combined = pd.concat(ras_list, axis=0)
-        # Normalize the RAS values by max RAS
-        ras_combined = ras_combined.div(ras_combined.max(axis=0))
-        ras_combined = ras_combined.fillna(0)
-
-
-    
-    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)
-
-    if(ARGS.medium_selector == "Custom"):
-        medium = read_dataset(ARGS.medium, "medium dataset")
-        medium.set_index(medium.columns[0], inplace=True)
-        medium = medium.astype(float)
-        medium = medium[medium.columns[0]].to_dict()
-    else:
-        df_mediums = pd.read_csv(ARGS.tool_dir + "/local/medium/medium.csv", index_col = 0)
-        ARGS.medium_selector = ARGS.medium_selector.replace("_", " ")
-        medium = df_mediums[[ARGS.medium_selector]]
-        medium = medium[ARGS.medium_selector].to_dict()
-
-    if(ARGS.ras_selector == True):
-        generate_bounds(model, medium, ras = ras_combined, output_folder=ARGS.output_folder)
-        class_assignments.to_csv(ARGS.cell_class, sep = '\t', index = False)
-    else:
-        generate_bounds(model, medium, output_folder=ARGS.output_folder)
-
-    pass
-        
-##############################################################################
-if __name__ == "__main__":
-    main()
\ No newline at end of file

--- a/COBRAxy/ras_to_bounds.xml	Sun Oct 13 11:33:51 2024 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,113 +0,0 @@
-<tool id="MaREA RAS to bounds" name="RAStoBounds" version="2.0.0">
-    
-    <macros>
-        <import>marea_macros.xml</import>
-    </macros>
-
-	<requirements>
-        <requirement type="package" version="1.24.4">numpy</requirement>
-        <requirement type="package" version="2.0.3">pandas</requirement>
-		<requirement type="package" version="0.29.0">cobra</requirement>
-        <requirement type="package" version="5.2.2">lxml</requirement>
-        <requirement type="package" version="1.4.2">joblib</requirement>
-	</requirements>
-
-    <command detect_errors="exit_code">
-        <![CDATA[
-      	python $__tool_directory__/ras_to_bounds.py
-        --tool_dir $__tool_directory__
-        --model_selector $cond_model.model_selector
-        --cell_class $cell_class
-        #if $cond_model.model_selector == 'Custom'
-            --model $model
-            --model_name $model.element_identifier
-        #end if
-        --medium_selector $cond_medium.medium_selector
-        #if $cond_medium.medium_selector == 'Custom'
-            --medium $medium
-        #end if
-        --ras_selector $cond_ras.ras_choice
-        #if $cond_ras.ras_choice == "True"
-        	--input_ras $cond_ras.input_ras
-            --classes $cond_ras.classes
-        #end if
-        --out_log $log
-        ]]>
-    </command>
-    <inputs>
-        <conditional name="cond_model">
-            <expand macro="options_ras_to_bounds_model"/>
-            <when value="Custom">
-                <param name="model" argument="--model" type="data" format="json, xml" label="Custom model" />
-            </when>
-        </conditional> 
-
-        <conditional name="cond_ras">
-			<param name="ras_choice" argument="--ras_choice" type="select" label="Do want to use RAS?">
-                	<option value="True" selected="true">Yes</option>
-                	<option value="False">No</option>
-        	</param>
-            <when value="True">
-                <param name="input_ras" argument="--input_ras" multiple="true" type="data" format="tabular, csv, tsv" label="RAS matrix:" />
-                <param name="classes" argument="--classes"  type="text"  value="None" label="Classes:" help="Write here the the classes to assign to each of the uploaded RAS matrices. Example for two RAS matrices:'cellNormal, cellCancer'. If you uploaded just one matrix, leave default value." />
-            </when>
-        </conditional>  
-        
-        <conditional name="cond_medium">
-            <expand macro="options_ras_to_bounds_medium"/>
-            <when value="Custom">
-                <param name="medium" argument="--medium" type="data" format="tabular, csv, tsv" label="Custom medium" />
-            </when>
-        </conditional> 
-
-    </inputs>
-
-    <outputs>
-        <data format="txt" name="log" label="RAStoBounds- Log" />
-        <data format="tabular" name="cell_class" label="RAStoBounds - Cells class" />
-        <collection name="ras_to_bounds" type="list" label="Ras to Bounds">
-            <discover_datasets name = "collection" pattern="__name_and_ext__" directory="ras_to_bounds"/>
-        </collection>
-
-    </outputs>
-
-    <help>
-
-    <![CDATA[
-
-What it does
--------------
-
-This tool generates the reactions bounds for a given metabolic model (JSON or XML format) both with and without the use of the Reaction Activity Scores (RAS) matrix generated by RAS generator.
-Moreover, it enables to use custom/pre-defined growth mediums to constrain exchange reactions. For a custom medium, It is suggested to use the template file returned by the Custom Data Generator tool.
-If the RAS matrix, generated by the RAS generator tool, is used, then a bounds file is generated for each cell. Otherwise, a single bounds file is returned.
-
-Accepted files:
-    - A model: JSON or XML file reporting reactions and rules contained in the model.   
-    - RAS matrix: tab-separated RAS file as returned by RAS generator. It can be a collection of RAS files too (e.g. one RAS matrix for normal cells and one for cancer cells). Note that if multiple RAs matrices are uploaded, the bounds are normalzed across all cells.
-    - Classes: If multiple RAS matrices are uploaded, then the tool returns a simple file containing for each cell the class it belongs to (cells coming from the same RAS matrix are assigned to the same class). This file is useful in the Flux enrichment analysis tool.
-    - Medium: tab-separated file containing lower and upper-bounds of medium reactions.
-
-Example of custum growth medium file:
-
-
-+------------+----------------+----------------+
-| Reaction ID|   lower_bound  |   upper_bound  |  
-+============+================+================+
-| r1         |    0.123167    |    0.371355    | 
-+------------+----------------+----------------+   
-| r2         |    0.268765    |    0.765567    |  
-+------------+----------------+----------------+   
-
-
-Output:
--------------
-
-The tool generates:
-    - bounds: reporting the bounds of the model, or cells if RAS is used. Format: tab-separated.
-    - Classes: a file containing the class of each cell (only if multiple RAS matrices were uploaded). Format: tab-separated.
-    - a log file (.txt).
-    ]]>
-    </help>
-    <expand macro="citations" />
-</tool>
\ No newline at end of file