# HG changeset patch
# User luca_milaz
# Date 1720260199 0
# Node ID 00a58a6695e06256a0ac41f2647a1492c8ae3dfe
# Parent  2778d3950ab7f63486c562b3ac2b1a4b005a9f3c
Uploaded

diff -r 2778d3950ab7 -r 00a58a6695e0 marea_2_0/model_generator.py
diff -r 2778d3950ab7 -r 00a58a6695e0 marea_2_0/model_generator.xml
diff -r 2778d3950ab7 -r 00a58a6695e0 marea_2_0/utils/model_generator.py
--- a/marea_2_0/utils/model_generator.py	Sat Jul 06 10:00:28 2024 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,125 +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
-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 = list(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('-ol', '--out_log', 
-                        help = "Output log")
-    
-    parser.add_argument('-td', '--tool_dir',
-                        type = str,
-                        required = True,
-                        help = 'your tool directory')
-    
-    parser.add_argument('-in', '--model',
-                        required = True,
-                        type=str,
-                        help = 'input model')
-    
-    
-    parser.add_argument('-im', '--input_medium',
-                        required = True,
-                        type=str,
-                        help = 'input medium')
-    
-    parser.add_argument('-ir', '--input_ras',
-                        required = True,
-                        type=str,
-                        help = 'input ras')
-    
-    parser.add_argument('-ot', '--output_type', 
-                        type = str,
-                        required = True,
-                        help = 'output type')
-    
-    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)
-
-
-def generate_model(cell_name, ras, medium):
-    # compute FVA 
-    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('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)
-
-    #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)
-
-    for index, row in ras.iterrows(): #iterate over cells RAS
-        generate_model(index, row, medium.loc[index])
-
-    pass
-        
-##############################################################################
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff -r 2778d3950ab7 -r 00a58a6695e0 marea_2_0/utils/model_generator.xml
--- a/marea_2_0/utils/model_generator.xml	Sat Jul 06 10:00:28 2024 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,83 +0,0 @@
-<tool id="modelGenerator" name="COBRAxy Model generator" version="2.0.0">
-    
-    <macros>
-        <import>marea_macros.xml</import>
-    </macros>
-
-	<requirements>
-        <requirement type="package" version="0.29.0">cobra</requirement>
-        <requirement type="package" version="4.9.3">lxml</requirement>
-        <requirement type="package" version="1.4.2">joblib</requirement>
-	</requirements>
-
-    <command detect_errors="exit_code">
-        <![CDATA[
-        python $__tool_directory__/model_generator.py
-        --model_selector $cond_model.model_selector
-        --tool_dir $__tool_directory__
-        --input_medium $input_medium
-        --input_ras $input_ras
-        --output_type "${",".join(map(str, $output_types))}"
-        --out_log $log
-        #if $cond_model.model_selector == 'Custom'
-            --model $model
-            --model_name $model.element_identifier
-        #end if
-        ]]>
-    </command>
-
-    <inputs>
-
-        <conditional name="cond_model">
-            <expand macro="options"/>
-            <when value="Custom">
-                <param name="model" argument="--model" type="data" format="json, xml" label="Custom modellll" />
-            </when>
-        </conditional>
-
-        <param name="input_ras" argument="--input_ras" multiple="false" type="data" format="tabular, csv, tsv" label="RAS matrix:" />
-
-        <param name="input_medium" argument="--input_medium" multiple="false" type="data" format="tabular, csv, tsv" label="Medium:"/>
-
-        <param type="select" argument="--output_types" multiple="true" name="output_types" label="Desired outputs">
-            <option value="FBA" selected="false">FBA</option>
-            <option value="pFBA" selected="false">pFBA</option>
-            <option value="FVA" selected="false">FVA</option>
-            <option value="sensitivity" selected="false">Sensitivity knock-out</option>
-        </param>
-        
-    </inputs>
-
-        		
-    <outputs>
-        <data format="txt" name="log" label="modelGenerator - Log" />
-        <collection name="results" type="list" label="${tool.name} - Results">
-            <discover_datasets pattern="__name_and_ext__" directory="model_generator"/>
-        </collection>
-    </outputs>
-       
-        
-    <help>
-    <![CDATA[
-What it does
--------------
-
-This tool generates flux samples starting from a model in JSON or XML format by using CBS (Corner-based sampling) and OPTGP (mproved Artificial Centering Hit-and-Run sampler) sampling algorithms.
-
-Accepted files:
-    - A model: JSON or XML file reporting reactions and rules contained in the model. It can be a single model, multiple models or a collection of models. 
-
-Output:
--------------
-
-The tool generates:
-    - Samples: reporting the sampled fluxes for each reaction. Format: csv or pickle.
-    - a log file (.txt).
-
-**TIP**: The Batches parameter is useful to mantain in memory just a batch of samples at time. For example, if you wish to sample 10.000 points, than it is suggested to select n_samples = 1.000 and n_batches=10.
-
-
-]]>
-    </help>
-    <expand macro="citations" />
-</tool>
\ No newline at end of file