annotate COBRAxy/src/flux_to_map.py @ 542:fcdbc81feb45 draft

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from __future__ import division
import csv
from enum import Enum
import re
import sys
import numpy as np
import pandas as pd
import itertools as it
import scipy.stats as st
import lxml.etree as ET
import math
try:
    from .utils import general_utils as utils
except:
    import utils.general_utils as utils
from PIL import Image
import os
import copy
import argparse
import pyvips
from PIL import Image
from typing import Tuple, Union, Optional, List, Dict
import matplotlib.pyplot as plt

ERRORS = []
########################## argparse ##########################################
ARGS :argparse.Namespace
def process_args(args:List[str] = None) -> argparse.Namespace:
    """
    Interfaces the script of a module with its frontend, making the user's choices for various parameters available as values in code.

    Args:
        args : Always obtained (in file) from sys.argv

    Returns:
        Namespace : An object containing the parsed arguments
    """
    parser = argparse.ArgumentParser(
        usage = "%(prog)s [options]",
        description = "process some value's genes to create a comparison's map.")
    
    #General:
    parser.add_argument(
        '-td', '--tool_dir',
        type = str,
        default = os.path.dirname(os.path.abspath(__file__)),
        help = 'your tool directory (default: auto-detected package location)')
    
    parser.add_argument('-on', '--control', type = str)
    parser.add_argument('-ol', '--out_log', help = "Output log")

    #Computation details:
    parser.add_argument(
        '-co', '--comparison',
        type = str, 
        default = 'manyvsmany',
        choices = ['manyvsmany', 'onevsrest', 'onevsmany'])

    parser.add_argument(
        '-te' ,'--test',
        type = str, 
        default = 'ks', 
        choices = ['ks', 'ttest_p', 'ttest_ind', 'wilcoxon', 'mw'],
        help = 'Statistical test to use (default: %(default)s)')
    
    parser.add_argument(
        '-pv' ,'--pValue',
        type = float, 
        default = 0.1, 
        help = 'P-Value threshold (default: %(default)s)')

    parser.add_argument(
        '-adj' ,'--adjusted',
        type = utils.Bool("adjusted"), default = False, 
        help = 'Apply the FDR (Benjamini-Hochberg) correction (default: %(default)s)')
    
    parser.add_argument(
        '-fc', '--fChange',
        type = float, 
        default = 1.5, 
        help = 'Fold-Change threshold (default: %(default)s)')
    
    parser.add_argument(
        '-op', '--option',
        type = str, 
        choices = ['datasets', 'dataset_class'],
        help='dataset or dataset and class')

    parser.add_argument(
        '-idf', '--input_data_fluxes',
        type = str,
        help = 'input dataset fluxes')
    
    parser.add_argument(
        '-icf', '--input_class_fluxes', 
        type = str,
        help = 'sample group specification fluxes')
    
    parser.add_argument(
        '-idsf', '--input_datas_fluxes', 
        type = str,
        nargs = '+', 
        help = 'input datasets fluxes')
    
    parser.add_argument(
        '-naf', '--names_fluxes', 
        type = str,
        nargs = '+', 
        help = 'input names fluxes')
    
    #Output:
    parser.add_argument(
        "-gs", "--generate_svg",
        type = utils.Bool("generate_svg"), default = True,
        help = "choose whether to generate svg")
    
    parser.add_argument(
        "-gp", "--generate_pdf",
        type = utils.Bool("generate_pdf"), default = True,
        help = "choose whether to generate pdf")
    
    parser.add_argument(
        '-cm', '--custom_map',
        type = str,
        help='custom map to use')
    
    parser.add_argument(
        '-mc',  '--choice_map',
        type = utils.Model, default = utils.Model.HMRcore,
        choices = [utils.Model.HMRcore, utils.Model.ENGRO2, utils.Model.Custom])
    
    parser.add_argument(
        '-colorm',  '--color_map',
        type = str,
        choices = ["jet", "viridis"])
    
    parser.add_argument(
        '-idop', '--output_path', 
        type = str,
        default='result',
        help = 'output path for maps')

    args :argparse.Namespace = parser.parse_args(args)
    args.net = True # TODO SICCOME I FLUSSI POSSONO ESSERE ANCHE NEGATIVI SONO SEMPRE CONSIDERATI NETTI

    return args

############################ dataset input ####################################
def read_dataset(data :str, name :str) -> pd.DataFrame:
    """
    Tries to read the dataset from its path (data) as a tsv and turns it into a DataFrame.

    Args:
        data : filepath of a dataset (from frontend input params or literals upon calling)
        name : name associated with the dataset (from frontend input params or literals upon calling)

    Returns:
        pd.DataFrame : dataset in a runtime operable shape
    
    Raises:
        sys.exit : if there's no data (pd.errors.EmptyDataError) or if the dataset has less than 2 columns
    """
    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

############################ dataset name #####################################
def name_dataset(name_data :str, count :int) -> str:
    """
    Produces a unique name for a dataset based on what was provided by the user. The default name for any dataset is "Dataset", thus if the user didn't change it this function appends f"_{count}" to make it unique.

    Args:
        name_data : name associated with the dataset (from frontend input params)
        count : counter from 1 to make these names unique (external)

    Returns:
        str : the name made unique
    """
    if str(name_data) == 'Dataset':
        return str(name_data) + '_' + str(count)
    else:
        return str(name_data)

############################ map_methods ######################################
FoldChange = Union[float, int, str] # Union[float, Literal[0, "-INF", "INF"]]
def fold_change(avg1 :float, avg2 :float) -> FoldChange:
    """
    Calculates the fold change between two gene expression values.

    Args:
        avg1 : average expression value from one dataset avg2 : average expression value from the other dataset

    Returns:
        FoldChange :
            0 : when both input values are 0
            "-INF" : when avg1 is 0
            "INF" : when avg2 is 0
            float : for any other combination of values
    """
    if avg1 == 0 and avg2 == 0:
        return 0
    elif avg1 == 0:
        return '-INF'
    elif avg2 == 0:
        return 'INF'
    else: # (threshold_F_C - 1) / (abs(threshold_F_C) + 1) con threshold_F_C > 1
        return (avg1 - avg2) / (abs(avg1) + abs(avg2))

def getElementById(reactionId :str, metabMap :ET.ElementTree) -> utils.Result[ET.Element, utils.Result.ResultErr]:
    """
    Finds any element in the given map with the given ID. ID uniqueness in an svg file is recommended but
    not enforced, if more than one element with the exact ID is found only the first will be returned.

    Args:
        reactionId (str): exact ID of the requested element.
        metabMap (ET.ElementTree): metabolic map containing the element.

    Returns:
        utils.Result[ET.Element, ResultErr]: result of the search, either the first match found or a ResultErr.
    """
    return utils.Result.Ok(
        f"//*[@id=\"{reactionId}\"]").map(
        lambda xPath : metabMap.xpath(xPath)[0]).mapErr(
        lambda _ : utils.Result.ResultErr(f"No elements with ID \"{reactionId}\" found in map"))
        # ^^^ we shamelessly ignore the contents of the IndexError, it offers nothing to the user.

def styleMapElement(element :ET.Element, styleStr :str) -> None:
    currentStyles :str = element.get("style", "")
    if re.search(r";stroke:[^;]+;stroke-width:[^;]+;stroke-dasharray:[^;]+$", currentStyles):
        currentStyles = ';'.join(currentStyles.split(';')[:-3])

    element.set("style", currentStyles + styleStr)

class ReactionDirection(Enum):
    Unknown = ""
    Direct  = "_F"
    Inverse = "_B"

    @classmethod
    def fromDir(cls, s :str) -> "ReactionDirection":
        # vvv as long as there's so few variants I actually condone the if spam:
        if s == ReactionDirection.Direct.value:  return ReactionDirection.Direct
        if s == ReactionDirection.Inverse.value: return ReactionDirection.Inverse
        return ReactionDirection.Unknown

    @classmethod
    def fromReactionId(cls, reactionId :str) -> "ReactionDirection":
        return ReactionDirection.fromDir(reactionId[-2:])

def getArrowBodyElementId(reactionId :str) -> str:
    if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV
    elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: reactionId = reactionId[:-2]
    return f"R_{reactionId}"

def getArrowHeadElementId(reactionId :str) -> Tuple[str, str]:
    """
    We attempt extracting the direction information from the provided reaction ID, if unsuccessful we provide the IDs of both directions.

    Args:
        reactionId : the provided reaction ID.

    Returns:
        Tuple[str, str]: either a single str ID for the correct arrow head followed by an empty string or both options to try.
    """
    if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV
    elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: return reactionId[:-3:-1] + reactionId[:-2], ""
    return f"F_{reactionId}", f"B_{reactionId}"

class ArrowColor(Enum):
    """
    Encodes possible arrow colors based on their meaning in the enrichment process.
    """
    Invalid       = "#BEBEBE" # gray, fold-change under treshold or not significant p-value
    Transparent   = "#ffffff00" # transparent, to make some arrow segments disappear
    UpRegulated   = "#ecac68" # red, up-regulated reaction
    DownRegulated = "#6495ed" # blue, down-regulated reaction

    UpRegulatedInv = "#FF0000"
    # ^^^ different shade of red (actually orange), up-regulated net value for a reversible reaction with
    # conflicting enrichment in the two directions.

    DownRegulatedInv = "#0000FF"
    # ^^^ different shade of blue (actually purple), down-regulated net value for a reversible reaction with
    # conflicting enrichment in the two directions.

    @classmethod
    def fromFoldChangeSign(cls, foldChange :float, *, useAltColor = False) -> "ArrowColor":
        colors = (cls.DownRegulated, cls.DownRegulatedInv) if foldChange < 0 else (cls.UpRegulated, cls.UpRegulatedInv)
        return colors[useAltColor]

    def __str__(self) -> str: return self.value

class Arrow:
    """
    Models the properties of a reaction arrow that change based on enrichment.
    """
    MIN_W = 2
    MAX_W = 12

    def __init__(self, width :int, col: ArrowColor, *, isDashed = False) -> None:
        """
        (Private) Initializes an instance of Arrow.

        Args:
            width : width of the arrow, ideally to be kept within Arrow.MIN_W and Arrow.MAX_W (not enforced).
            col : color of the arrow.
            isDashed : whether the arrow should be dashed, meaning the associated pValue resulted not significant.
        
        Returns:
            None : practically, a Arrow instance.
        """
        self.w    = width
        self.col  = col
        self.dash = isDashed
    
    def applyTo(self, reactionId :str, metabMap :ET.ElementTree, styleStr :str) -> None:
        if getElementById(reactionId, metabMap).map(lambda el : styleMapElement(el, styleStr)).isErr:
            ERRORS.append(reactionId)

    def styleReactionElements(self, metabMap :ET.ElementTree, reactionId :str, *, mindReactionDir = True) -> None:
        if not mindReactionDir:
            return self.applyTo(getArrowBodyElementId(reactionId), metabMap, self.toStyleStr())
        
        # Now we style the arrow head(s):
        idOpt1, idOpt2 = getArrowHeadElementId(reactionId)
        self.applyTo(idOpt1, metabMap, self.toStyleStr(downSizedForTips = True))
        if idOpt2: self.applyTo(idOpt2, metabMap, self.toStyleStr(downSizedForTips = True))

    def styleReactionElementsMeanMedian(self, metabMap :ET.ElementTree, reactionId :str, isNegative:bool) -> None:

        self.applyTo(getArrowBodyElementId(reactionId), metabMap, self.toStyleStr())
        idOpt1, idOpt2 = getArrowHeadElementId(reactionId)

        if(isNegative):
            self.applyTo(idOpt2, metabMap, self.toStyleStr(downSizedForTips = True))
            self.col = ArrowColor.Transparent
            self.applyTo(idOpt1, metabMap, self.toStyleStr(downSizedForTips = True)) #trasp
        else:
            self.applyTo(idOpt1, metabMap, self.toStyleStr(downSizedForTips = True))
            self.col = ArrowColor.Transparent
            self.applyTo(idOpt2, metabMap, self.toStyleStr(downSizedForTips = True)) #trasp


    
    def getMapReactionId(self, reactionId :str, mindReactionDir :bool) -> str:
        """
        Computes the reaction ID as encoded in the map for a given reaction ID from the dataset.

        Args:
            reactionId: the reaction ID, as encoded in the dataset.
            mindReactionDir: if True forward (F_) and backward (B_) directions will be encoded in the result.
    
        Returns:
            str : the ID of an arrow's body or tips in the map.
        """
        # we assume the reactionIds also don't encode reaction dir if they don't mind it when styling the map.
        if not mindReactionDir: return "R_" + reactionId

        #TODO: this is clearly something we need to make consistent in fluxes
        return (reactionId[:-3:-1] + reactionId[:-2]) if reactionId[:-2] in ["_F", "_B"] else f"F_{reactionId}" # "Pyr_F" --> "F_Pyr"

    def toStyleStr(self, *, downSizedForTips = False) -> str:
        """
        Collapses the styles of this Arrow into a str, ready to be applied as part of the "style" property on an svg element.

        Returns:
            str : the styles string.
        """
        width = self.w
        if downSizedForTips: width *= 0.8
        return f";stroke:{self.col};stroke-width:{width};stroke-dasharray:{'5,5' if self.dash else 'none'}"

# vvv These constants could be inside the class itself a static properties, but python
# was built by brainless organisms so here we are!
INVALID_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid)
INSIGNIFICANT_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid, isDashed = True)

def applyFluxesEnrichmentToMap(fluxesEnrichmentRes :Dict[str, Union[Tuple[float, FoldChange], Tuple[float, FoldChange, float, float]]], metabMap :ET.ElementTree, maxNumericZScore :float) -> None:
    """
    Applies fluxes enrichment results to the provided metabolic map.

    Args:
        fluxesEnrichmentRes : fluxes enrichment results.
        metabMap : the metabolic map to edit.
        maxNumericZScore : biggest finite z-score value found.
    
    Side effects:
        metabMap : mut
    
    Returns:
        None
    """
    for reactionId, values in fluxesEnrichmentRes.items():
        pValue = values[0]
        foldChange = values[1]
        z_score = values[2]

        if math.isnan(pValue) or (isinstance(foldChange, float) and math.isnan(foldChange)):
            continue

        if isinstance(foldChange, str): foldChange = float(foldChange)
        if pValue > ARGS.pValue: # pValue above tresh: dashed arrow
            INSIGNIFICANT_ARROW.styleReactionElements(metabMap, reactionId)
            INSIGNIFICANT_ARROW.styleReactionElements(metabMap, reactionId, mindReactionDir = False)

            continue

        if abs(foldChange) <  (ARGS.fChange - 1) / (abs(ARGS.fChange) + 1):
            INVALID_ARROW.styleReactionElements(metabMap, reactionId)
            INVALID_ARROW.styleReactionElements(metabMap, reactionId, mindReactionDir = False)

            continue
        
        width = Arrow.MAX_W
        if not math.isinf(z_score):
            try: 
                width = min(
                    max(abs(z_score * Arrow.MAX_W) / maxNumericZScore, Arrow.MIN_W),
                    Arrow.MAX_W) 

            except ZeroDivisionError: pass
        # TODO CHECK RV
        #if not reactionId.endswith("_RV"): # RV stands for reversible reactions
        #   Arrow(width, ArrowColor.fromFoldChangeSign(foldChange)).styleReactionElements(metabMap, reactionId)
        #   continue
        
        #reactionId = reactionId[:-3] # Remove "_RV"
        
        inversionScore = (values[3] < 0) + (values[4] < 0) # Compacts the signs of averages into 1 easy to check score
        if inversionScore == 2: foldChange *= -1
        # ^^^ Style the inverse direction with the opposite sign netValue
        
        # If the score is 1 (opposite signs) we use alternative colors vvv
        arrow = Arrow(width, ArrowColor.fromFoldChangeSign(foldChange, useAltColor = inversionScore == 1))
        
        # vvv These 2 if statements can both be true and can both happen
        if ARGS.net: # style arrow head(s):
            arrow.styleReactionElements(metabMap, reactionId + ("_B" if inversionScore == 2 else "_F"))
            arrow.applyTo(("F_" if inversionScore == 2 else "B_") + reactionId, metabMap, f";stroke:{ArrowColor.Transparent};stroke-width:0;stroke-dasharray:None")

        arrow.styleReactionElements(metabMap, reactionId, mindReactionDir = False)


############################ split class ######################################
def split_class(classes :pd.DataFrame, resolve_rules :Dict[str, List[float]]) -> Dict[str, List[List[float]]]:
    """
    Generates a :dict that groups together data from a :DataFrame based on classes the data is related to.

    Args:
        classes : a :DataFrame of only string values, containing class information (rows) and keys to query the resolve_rules :dict
        resolve_rules : a :dict containing :float data

    Returns:
        dict : the dict with data grouped by class

    Side effects:
        classes : mut
    """
    class_pat :Dict[str, List[List[float]]] = {}
    for i in range(len(classes)):
        classe :str = classes.iloc[i, 1]
        if pd.isnull(classe): continue

        l :List[List[float]] = []
        for j in range(i, len(classes)):
            if classes.iloc[j, 1] == classe:
                pat_id :str = classes.iloc[j, 0]
                tmp = resolve_rules.get(pat_id, None)
                if tmp != None:
                    l.append(tmp)
                classes.iloc[j, 1] = None
        
        if l:
            class_pat[classe] = list(map(list, zip(*l)))
            continue
        
        utils.logWarning(
            f"Warning: no sample found in class \"{classe}\", the class has been disregarded", ARGS.out_log)
    
    return class_pat

############################ conversion ##############################################
#conversion from svg to png 
def svg_to_png_with_background(svg_path :utils.FilePath, png_path :utils.FilePath, dpi :int = 72, scale :int = 1, size :Optional[float] = None) -> None:
    """
    Internal utility to convert an SVG to PNG (forced opaque) to aid in PDF conversion.

    Args:
        svg_path : path to SVG file
        png_path : path for new PNG file
        dpi : dots per inch of the generated PNG
        scale : scaling factor for the generated PNG, computed internally when a size is provided
        size : final effective width of the generated PNG

    Returns:
        None
    """
    if size:
        image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=1)
        scale = size / image.width
        image = image.resize(scale)
    else:
        image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=scale)

    white_background = pyvips.Image.black(image.width, image.height).new_from_image([255, 255, 255])
    white_background = white_background.affine([scale, 0, 0, scale])

    if white_background.bands != image.bands:
        white_background = white_background.extract_band(0)

    composite_image = white_background.composite2(image, 'over')
    composite_image.write_to_file(png_path.show())

#funzione unica, lascio fuori i file e li passo in input
#conversion from png to pdf
def convert_png_to_pdf(png_file :utils.FilePath, pdf_file :utils.FilePath) -> None:
    """
    Internal utility to convert a PNG to PDF to aid from SVG conversion.

    Args:
        png_file : path to PNG file
        pdf_file : path to new PDF file

    Returns:
        None
    """
    image = Image.open(png_file.show())
    image = image.convert("RGB")
    image.save(pdf_file.show(), "PDF", resolution=100.0)

#function called to reduce redundancy in the code
def convert_to_pdf(file_svg :utils.FilePath, file_png :utils.FilePath, file_pdf :utils.FilePath) -> None:
    """
    Converts the SVG map at the provided path to PDF.

    Args:
        file_svg : path to SVG file
        file_png : path to PNG file
        file_pdf : path to new PDF file

    Returns:
        None
    """
    svg_to_png_with_background(file_svg, file_png)
    try:
        convert_png_to_pdf(file_png, file_pdf)
        print(f'PDF file {file_pdf.filePath} successfully generated.')
    
    except Exception as e:
        raise utils.DataErr(file_pdf.show(), f'Error generating PDF file: {e}')

############################ map ##############################################
def buildOutputPath(dataset1Name :str, dataset2Name = "rest", *, details = "", ext :utils.FileFormat) -> utils.FilePath:
    """
    Builds a FilePath instance from the names of confronted datasets ready to point to a location in the
    "result/" folder, used by this tool for output files in collections.

    Args:
        dataset1Name : _description_
        dataset2Name : _description_. Defaults to "rest".
        details : _description_
        ext : _description_

    Returns:
        utils.FilePath : _description_
    """
    # This function returns a util data structure but is extremely specific to this module.
    # RAS also uses collections as output and as such might benefit from a method like this, but I'd wait
    # TODO: until a third tool with multiple outputs appears before porting this to utils.
    return utils.FilePath(
        f"{dataset1Name}_vs_{dataset2Name}" + (f" ({details})" if details else ""),
        # ^^^ yes this string is built every time even if the form is the same for the same 2 datasets in
        # all output files: I don't care, this was never the performance bottleneck of the tool and
        # there is no other net gain in saving and re-using the built string.
        ext,
        prefix = ARGS.output_path)

FIELD_NOT_AVAILABLE = '/'
def writeToCsv(rows: List[list], fieldNames :List[str], outPath :utils.FilePath) -> None:
    fieldsAmt = len(fieldNames)
    with open(outPath.show(), "w", newline = "") as fd:
        writer = csv.DictWriter(fd, fieldnames = fieldNames, delimiter = '\t')
        writer.writeheader()
        
        for row in rows:
            sizeMismatch = fieldsAmt - len(row)
            if sizeMismatch > 0: row.extend([FIELD_NOT_AVAILABLE] * sizeMismatch)
            writer.writerow({ field : data for field, data in zip(fieldNames, row) })

OldEnrichedScores = Dict[str, List[Union[float, FoldChange]]] #TODO: try to use Tuple whenever possible
def writeTabularResult(enrichedScores : OldEnrichedScores, outPath :utils.FilePath) -> None:
    fieldNames = ["ids", "P_Value", "fold change", "z-score"]
    fieldNames.extend(["average_1", "average_2"])

    writeToCsv([ [reactId] + values for reactId, values in enrichedScores.items() ], fieldNames, outPath)

def temp_thingsInCommon(tmp :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, max_z_score :float, dataset1Name :str, dataset2Name = "rest") -> None:
    # this function compiles the things always in common between comparison modes after enrichment.
    # TODO: organize, name better.
    writeTabularResult(tmp, buildOutputPath(dataset1Name, dataset2Name, details = "Tabular Result", ext = utils.FileFormat.TSV))
    for reactId, enrichData in tmp.items(): tmp[reactId] = tuple(enrichData)
    applyFluxesEnrichmentToMap(tmp, core_map, max_z_score)

def computePValue(dataset1Data: List[float], dataset2Data: List[float]) -> Tuple[float, float]:
    """
    Computes the statistical significance score (P-value) of the comparison between coherent data
    from two datasets. The data is supposed to, in both datasets:
    - be related to the same reaction ID;
    - be ordered by sample, such that the item at position i in both lists is related to the
      same sample or cell line.

    Args:
        dataset1Data : data from the 1st dataset.
        dataset2Data : data from the 2nd dataset.

    Returns:
        tuple: (P-value, Z-score)
            - P-value from the selected test on the provided data.
            - Z-score of the difference between means of the two datasets.
    """

    match ARGS.test:
        case "ks":
            # Perform Kolmogorov-Smirnov test
            _, p_value = st.ks_2samp(dataset1Data, dataset2Data)
        case "ttest_p":
            # Datasets should have same size
            if len(dataset1Data) != len(dataset2Data):
                raise ValueError("Datasets must have the same size for paired t-test.")
            # Perform t-test for paired samples
            _, p_value = st.ttest_rel(dataset1Data, dataset2Data)
        case "ttest_ind":
            # Perform t-test for independent samples
            _, p_value = st.ttest_ind(dataset1Data, dataset2Data)
        case "wilcoxon":
            # Datasets should have same size
            if len(dataset1Data) != len(dataset2Data):
                raise ValueError("Datasets must have the same size for Wilcoxon signed-rank test.")
            # Perform Wilcoxon signed-rank test
            np.random.seed(42)  # Ensure reproducibility since zsplit method is used
            _, p_value = st.wilcoxon(dataset1Data, dataset2Data, zero_method="zsplit")
        case "mw":
            # Perform Mann-Whitney U test
            _, p_value = st.mannwhitneyu(dataset1Data, dataset2Data)
    
    # Calculate means and standard deviations
    mean1 = np.nanmean(dataset1Data)
    mean2 = np.nanmean(dataset2Data)
    std1 = np.nanstd(dataset1Data, ddof=1)
    std2 = np.nanstd(dataset2Data, ddof=1)
    
    n1 = len(dataset1Data)
    n2 = len(dataset2Data)
    
    # Calculate Z-score
    z_score = (mean1 - mean2) / np.sqrt((std1**2 / n1) + (std2**2 / n2))
    
    return p_value, z_score

def compareDatasetPair(dataset1Data :List[List[float]], dataset2Data :List[List[float]], ids :List[str]) -> Tuple[Dict[str, List[Union[float, FoldChange]]], float]:
    #TODO: the following code still suffers from "dumbvarnames-osis"
    comparisonResult :Dict[str, List[Union[float, FoldChange]]] = {}
    count   = 0
    max_z_score = 0
    for l1, l2 in zip(dataset1Data, dataset2Data):
        reactId = ids[count]
        count += 1
        if not reactId: continue # we skip ids that have already been processed

        try: 
            p_value, z_score = computePValue(l1, l2)
            avg1 = sum(l1) / len(l1)
            avg2 = sum(l2) / len(l2)
            f_c = fold_change(avg1, avg2)
            if np.isfinite(z_score) and max_z_score < abs(z_score): max_z_score = abs(z_score)
            
            comparisonResult[reactId] = [float(p_value), f_c, z_score, avg1, avg2]
        except (TypeError, ZeroDivisionError): continue

    # Apply multiple testing correction if set by the user
    if ARGS.adjusted:
        
        # Retrieve the p-values from the comparisonResult dictionary, they have to be different from NaN
        validPValues = [(reactId, result[0]) for reactId, result in comparisonResult.items() if not np.isnan(result[0])]

        if not validPValues:
            return comparisonResult, max_z_score
        
        # Unpack the valid p-values
        reactIds, pValues = zip(*validPValues)
        # Adjust the p-values using the Benjamini-Hochberg method
        adjustedPValues = st.false_discovery_control(pValues)
        # Update the comparisonResult dictionary with the adjusted p-values
        for reactId , adjustedPValue in zip(reactIds, adjustedPValues):
            comparisonResult[reactId][0] = adjustedPValue
    
    return comparisonResult, max_z_score

def computeEnrichment(class_pat :Dict[str, List[List[float]]], ids :List[str]) -> List[Tuple[str, str, dict, float]]:
    """
    Compares clustered data based on a given comparison mode and applies enrichment-based styling on the
    provided metabolic map.

    Args:
        class_pat : the clustered data.
        ids : ids for data association.
        

    Returns:
        List[Tuple[str, str, dict, float]]: List of tuples with pairs of dataset names, comparison dictionary, and max z-score.

    Raises:
        sys.exit : if there are less than 2 classes for comparison

    """
    class_pat = { k.strip() : v for k, v in class_pat.items() }
    #TODO: simplfy this stuff vvv and stop using sys.exit (raise the correct utils error)
    if (not class_pat) or (len(class_pat.keys()) < 2): sys.exit('Execution aborted: classes provided for comparisons are less than two\n')

    enrichment_results = []

    
    if ARGS.comparison == "manyvsmany":
        for i, j in it.combinations(class_pat.keys(), 2):
            comparisonDict, max_z_score = compareDatasetPair(class_pat.get(i), class_pat.get(j), ids)
            enrichment_results.append((i, j, comparisonDict, max_z_score))
    
    elif ARGS.comparison == "onevsrest":
        for single_cluster in class_pat.keys():
            rest = [item for k, v in class_pat.items() if k != single_cluster for item in v]

            comparisonDict, max_z_score = compareDatasetPair(class_pat.get(single_cluster), rest, ids)
            enrichment_results.append((single_cluster, "rest", comparisonDict, max_z_score))
    
    #elif ARGS.comparison == "onevsmany":
    #    controlItems = class_pat.get(ARGS.control)
    #    for otherDataset in class_pat.keys():
    #        if otherDataset == ARGS.control:
    #            continue
    #        comparisonDict, max_z_score = compareDatasetPair(controlItems, class_pat.get(otherDataset), ids)
    #        enrichment_results.append((ARGS.control, otherDataset, comparisonDict, max_z_score))
    elif ARGS.comparison == "onevsmany":
        controlItems = class_pat.get(ARGS.control)
        for otherDataset in class_pat.keys():
            if otherDataset == ARGS.control:
                continue
            comparisonDict, max_z_score = compareDatasetPair(class_pat.get(otherDataset),controlItems, ids)
            enrichment_results.append(( otherDataset,ARGS.control, comparisonDict, max_z_score))

    return enrichment_results

def createOutputMaps(dataset1Name :str, dataset2Name :str, core_map :ET.ElementTree) -> None:
    svgFilePath = buildOutputPath(dataset1Name, dataset2Name, details="SVG Map", ext=utils.FileFormat.SVG)
    utils.writeSvg(svgFilePath, core_map)

    if ARGS.generate_pdf:
        pngPath = buildOutputPath(dataset1Name, dataset2Name, details="PNG Map", ext=utils.FileFormat.PNG)
        pdfPath = buildOutputPath(dataset1Name, dataset2Name, details="PDF Map", ext=utils.FileFormat.PDF)
        convert_to_pdf(svgFilePath, pngPath, pdfPath)

    if not ARGS.generate_svg:
        os.remove(svgFilePath.show())

ClassPat = Dict[str, List[List[float]]]
def getClassesAndIdsFromDatasets(datasetsPaths :List[str], datasetPath :str, classPath :str, names :List[str]) -> Tuple[List[str], ClassPat]:
    # TODO: I suggest creating dicts with ids as keys instead of keeping class_pat and ids separate,
    # for the sake of everyone's sanity.
    class_pat :ClassPat = {}
    if ARGS.option == 'datasets':
        num = 1 #TODO: the dataset naming function could be a generator
        for path, name in zip(datasetsPaths, names):
            name = name_dataset(name, num)
            resolve_rules_float, ids = getDatasetValues(path, name)
            if resolve_rules_float != None:
                class_pat[name] = list(map(list, zip(*resolve_rules_float.values())))
        
            num += 1
    
    elif ARGS.option == "dataset_class":
        classes = read_dataset(classPath, "class")
        classes = classes.astype(str)
        resolve_rules_float, ids = getDatasetValues(datasetPath, "Dataset Class (not actual name)")
        #check if classes have match on ids
        if not all(classes.iloc[:, 0].isin(ids)):
            utils.logWarning(
            "No match between classes and sample IDs", ARGS.out_log)
        if resolve_rules_float != None: class_pat = split_class(classes, resolve_rules_float)
    
    return ids, class_pat
    #^^^ TODO: this could be a match statement over an enum, make it happen future marea dev with python 3.12! (it's why I kept the ifs)

#TODO: create these damn args as FilePath objects
def getDatasetValues(datasetPath :str, datasetName :str) -> Tuple[ClassPat, List[str]]:
    """
    Opens the dataset at the given path and extracts the values (expected nullable numerics) and the IDs.

    Args:
        datasetPath : path to the dataset
        datasetName (str): dataset name, used in error reporting

    Returns:
        Tuple[ClassPat, List[str]]: values and IDs extracted from the dataset
    """
    dataset = read_dataset(datasetPath, datasetName)
    
    # Ensure the first column is treated as the reaction name
    dataset = dataset.set_index(dataset.columns[0])

    # Check if required reactions exist in the dataset
    required_reactions = ['EX_lac__L_e', 'EX_glc__D_e', 'EX_gln__L_e', 'EX_glu__L_e']
    missing_reactions = [reaction for reaction in required_reactions if reaction not in dataset.index]

    if missing_reactions:
        sys.exit(f'Execution aborted: Missing required reactions {missing_reactions} in {datasetName}\n')

    # Calculate new rows using safe division
    lact_glc = np.divide(
        np.clip(dataset.loc['EX_lac__L_e'].to_numpy(), a_min=0, a_max=None),
        np.clip(dataset.loc['EX_glc__D_e'].to_numpy(), a_min=None, a_max=0),
        out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan),  # Prepara un array con NaN come output di default
        where=dataset.loc['EX_glc__D_e'].to_numpy() != 0  # Condizione per evitare la divisione per zero
    )
    lact_gln = np.divide(
        np.clip(dataset.loc['EX_lac__L_e'].to_numpy(), a_min=0, a_max=None),
        np.clip(dataset.loc['EX_gln__L_e'].to_numpy(), a_min=None, a_max=0),
        out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan), 
        where=dataset.loc['EX_gln__L_e'].to_numpy() != 0
    )
    lact_o2 = np.divide(
        np.clip(dataset.loc['EX_lac__L_e'].to_numpy(), a_min=0, a_max=None),
        np.clip(dataset.loc['EX_o2_e'].to_numpy(), a_min=None, a_max=0),
        out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan), 
        where=dataset.loc['EX_o2_e'].to_numpy() != 0
    )
    glu_gln = np.divide(
        dataset.loc['EX_glu__L_e'].to_numpy(),
        np.clip(dataset.loc['EX_gln__L_e'].to_numpy(), a_min=None, a_max=0), 
        out=np.full_like(dataset.loc['EX_lac__L_e'].to_numpy(), np.nan),
        where=dataset.loc['EX_gln__L_e'].to_numpy() != 0
    )


    values = {'lact_glc': lact_glc, 'lact_gln': lact_gln, 'lact_o2': lact_o2, 'glu_gln': glu_gln}
   
    # Sostituzione di inf e NaN con 0 se necessario
    for key in values:
        values[key] = np.nan_to_num(values[key], nan=0.0, posinf=0.0, neginf=0.0)

    # Creazione delle nuove righe da aggiungere al dataset
    new_rows = pd.DataFrame({
        dataset.index.name: ['LactGlc', 'LactGln', 'LactO2', 'GluGln'],
        **{col: [values['lact_glc'][i], values['lact_gln'][i], values['lact_o2'][i], values['glu_gln'][i]] 
           for i, col in enumerate(dataset.columns)}
    })

    #print(new_rows)

    # Ritorna il dataset originale con le nuove righe
    dataset.reset_index(inplace=True)
    dataset = pd.concat([dataset, new_rows], ignore_index=True)

    IDs = pd.Series.tolist(dataset.iloc[:, 0].astype(str))

    dataset = dataset.drop(dataset.columns[0], axis = "columns").to_dict("list")
    return { id : list(map(utils.Float("Dataset values, not an argument"), values)) for id, values in dataset.items() }, IDs

def rgb_to_hex(rgb):
    """
    Convert RGB values (0-1 range) to hexadecimal color format.

    Args:
        rgb (numpy.ndarray): An array of RGB color components (in the range [0, 1]).

    Returns:
        str: The color in hexadecimal format (e.g., '#ff0000' for red).
    """
    # Convert RGB values (0-1 range) to hexadecimal format
    rgb = (np.array(rgb) * 255).astype(int)
    return '#{:02x}{:02x}{:02x}'.format(rgb[0], rgb[1], rgb[2])

def save_colormap_image(min_value: float, max_value: float, path: utils.FilePath, colorMap:str="viridis"):
    """
    Create and save an image of the colormap showing the gradient and its range.

    Args:
        min_value (float): The minimum value of the colormap range.
        max_value (float): The maximum value of the colormap range.
        filename (str): The filename for saving the image.
    """

    # Create a colormap using matplotlib
    cmap = plt.get_cmap(colorMap)

    # Create a figure and axis
    fig, ax = plt.subplots(figsize=(6, 1))
    fig.subplots_adjust(bottom=0.5)

    # Create a gradient image
    gradient = np.linspace(0, 1, 256)
    gradient = np.vstack((gradient, gradient))

    # Add min and max value annotations
    ax.text(0, 0.5, f'{np.round(min_value, 3)}', va='center', ha='right', transform=ax.transAxes, fontsize=12, color='black')
    ax.text(1, 0.5, f'{np.round(max_value, 3)}', va='center', ha='left', transform=ax.transAxes, fontsize=12, color='black')


    # Display the gradient image
    ax.imshow(gradient, aspect='auto', cmap=cmap)
    ax.set_axis_off()

    # Save the image
    plt.savefig(path.show(), bbox_inches='tight', pad_inches=0)
    plt.close()
    pass

def min_nonzero_abs(arr):
    # Flatten the array and filter out zeros, then find the minimum of the remaining values
    non_zero_elements = np.abs(arr)[np.abs(arr) > 0]
    return np.min(non_zero_elements) if non_zero_elements.size > 0 else None

def computeEnrichmentMeanMedian(metabMap: ET.ElementTree, class_pat: Dict[str, List[List[float]]], ids: List[str], colormap:str) -> None:
    """
    Compute and visualize the metabolic map based on mean and median of the input fluxes.
    The fluxes are normalised across classes/datasets and visualised using the given colormap.

    Args:
        metabMap (ET.ElementTree): An XML tree representing the metabolic map.
        class_pat (Dict[str, List[List[float]]]): A dictionary where keys are class names and values are lists of enrichment values.
        ids (List[str]): A list of reaction IDs to be used for coloring arrows.
    
    Returns:
        None
    """
    # Create copies only if they are needed
    metabMap_mean = copy.deepcopy(metabMap)
    metabMap_median = copy.deepcopy(metabMap)

    # Compute medians and means
    medians = {key: np.round(np.nanmedian(np.array(value), axis=1), 6) for key, value in class_pat.items()}
    means = {key: np.round(np.nanmean(np.array(value), axis=1),6) for key, value in class_pat.items()}

    # Normalize medians and means
    max_flux_medians = max(np.max(np.abs(arr)) for arr in medians.values())
    max_flux_means = max(np.max(np.abs(arr)) for arr in means.values())

    min_flux_medians = min(min_nonzero_abs(arr) for arr in medians.values())
    min_flux_means = min(min_nonzero_abs(arr) for arr in means.values())

    medians = {key: median/max_flux_medians for key, median in medians.items()}
    means = {key: mean/max_flux_means for key, mean in means.items()}

    save_colormap_image(min_flux_medians, max_flux_medians, utils.FilePath("Color map median", ext=utils.FileFormat.PNG, prefix=ARGS.output_path), colormap)
    save_colormap_image(min_flux_means, max_flux_means, utils.FilePath("Color map mean", ext=utils.FileFormat.PNG, prefix=ARGS.output_path), colormap)

    cmap = plt.get_cmap(colormap)

    min_width = 2.0  # Minimum arrow width
    max_width = 15.0  # Maximum arrow width

    for key in class_pat:
        # Create color mappings for median and mean
        colors_median = {
            rxn_id: rgb_to_hex(cmap(abs(medians[key][i]))) if medians[key][i] != 0 else '#bebebe'  #grey blocked
            for i, rxn_id in enumerate(ids)
        }

        colors_mean = {
            rxn_id: rgb_to_hex(cmap(abs(means[key][i]))) if means[key][i] != 0 else '#bebebe'  #grey blocked
            for i, rxn_id in enumerate(ids)
        }

        for i, rxn_id in enumerate(ids):
            # Calculate arrow width for median
            width_median = np.interp(abs(medians[key][i]), [0, 1], [min_width, max_width])
            isNegative = medians[key][i] < 0
            apply_arrow(metabMap_median, rxn_id, colors_median[rxn_id], isNegative, width_median)

            # Calculate arrow width for mean
            width_mean = np.interp(abs(means[key][i]), [0, 1], [min_width, max_width])
            isNegative = means[key][i] < 0
            apply_arrow(metabMap_mean, rxn_id, colors_mean[rxn_id], isNegative, width_mean)

        # Save and convert the SVG files
        save_and_convert(metabMap_mean, "mean", key)
        save_and_convert(metabMap_median, "median", key)

def apply_arrow(metabMap, rxn_id, color, isNegative, width=5):
    """
    Apply an arrow to a specific reaction in the metabolic map with a given color.

    Args:
        metabMap (ET.ElementTree): An XML tree representing the metabolic map.
        rxn_id (str): The ID of the reaction to which the arrow will be applied.
        color (str): The color of the arrow in hexadecimal format.
        isNegative (bool): A boolean indicating if the arrow represents a negative value.
        width (int): The width of the arrow.

    Returns:
        None
    """
    arrow = Arrow(width=width, col=color)
    arrow.styleReactionElementsMeanMedian(metabMap, rxn_id, isNegative)
    pass

def save_and_convert(metabMap, map_type, key):
    """
    Save the metabolic map as an SVG file and optionally convert it to PNG and PDF formats.

    Args:
        metabMap (ET.ElementTree): An XML tree representing the metabolic map.
        map_type (str): The type of map ('mean' or 'median').
        key (str): The key identifying the specific map.

    Returns:
        None
    """
    svgFilePath = utils.FilePath(f"SVG Map {map_type} - {key}", ext=utils.FileFormat.SVG, prefix=ARGS.output_path)
    utils.writeSvg(svgFilePath, metabMap)
    if ARGS.generate_pdf:
        pngPath = utils.FilePath(f"PNG Map {map_type} - {key}", ext=utils.FileFormat.PNG, prefix=ARGS.output_path)
        pdfPath = utils.FilePath(f"PDF Map {map_type} - {key}", ext=utils.FileFormat.PDF, prefix=ARGS.output_path)
        convert_to_pdf(svgFilePath, pngPath, pdfPath)
    if not ARGS.generate_svg:
        os.remove(svgFilePath.show())

############################ MAIN #############################################
def main(args:List[str] = None) -> None:
    """
    Initializes everything and sets the program in motion based on the fronted input arguments.

    Returns:
        None
    
    Raises:
        sys.exit : if a user-provided custom map is in the wrong format (ET.XMLSyntaxError, ET.XMLSchemaParseError)
    """

    global ARGS
    ARGS = process_args(args)

    if ARGS.custom_map == 'None':
        ARGS.custom_map = None

    if os.path.isdir(ARGS.output_path) == False: os.makedirs(ARGS.output_path)
    
    core_map :ET.ElementTree = ARGS.choice_map.getMap(
        ARGS.tool_dir,
        utils.FilePath.fromStrPath(ARGS.custom_map) if ARGS.custom_map else None)
    # TODO: ^^^ ugly but fine for now, the argument is None if the model isn't custom because no file was given.
    # getMap will None-check the customPath and panic when the model IS custom but there's no file (good). A cleaner
    # solution can be derived from my comment in FilePath.fromStrPath

    ids, class_pat = getClassesAndIdsFromDatasets(ARGS.input_datas_fluxes, ARGS.input_data_fluxes, ARGS.input_class_fluxes, ARGS.names_fluxes)

    if(ARGS.choice_map == utils.Model.HMRcore):
        temp_map = utils.Model.HMRcore_no_legend
        computeEnrichmentMeanMedian(temp_map.getMap(ARGS.tool_dir), class_pat, ids, ARGS.color_map)
    elif(ARGS.choice_map == utils.Model.ENGRO2):
        temp_map = utils.Model.ENGRO2_no_legend
        computeEnrichmentMeanMedian(temp_map.getMap(ARGS.tool_dir), class_pat, ids, ARGS.color_map)
    else:
        computeEnrichmentMeanMedian(core_map, class_pat, ids, ARGS.color_map)


    enrichment_results = computeEnrichment(class_pat, ids)
    for i, j, comparisonDict, max_z_score in enrichment_results:
        map_copy = copy.deepcopy(core_map)
        temp_thingsInCommon(comparisonDict, map_copy, max_z_score, i, j)
        createOutputMaps(i, j, map_copy)
    
    if not ERRORS: return
    utils.logWarning(
        f"The following reaction IDs were mentioned in the dataset but weren't found in the map: {ERRORS}",
        ARGS.out_log)
    
    print('Execution succeded')

###############################################################################
if __name__ == "__main__":
    main()