comparison COBRAxy/marea.py @ 286:eaa623b4b703 draft

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285:216a796a967a

    #Computation details:
    parser.add_argument(
        '-co', '--comparison',
        type = str, 
        default = '1vs1',
        choices = ['manyvsmany', 'onevsrest', 'onevsmany'])
    
    parser.add_argument(
        '-pv' ,'--pValue',
        type = float, 

        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.

            "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 fix_style(l :str, col :Optional[str], width :str, dash :str) -> str:
    """
    Produces a "fixed" style string to assign to a reaction arrow in the SVG map, assigning style properties to the corresponding values passed as input params.

    Args:

        tmp.append('stroke-width:' + width)
    if not flag_dash:
        tmp.append('stroke-dasharray:' + dash)
    return ';'.join(tmp)

# The type of d values is collapsed, losing precision, because the dict containst lists instead of tuples, please fix!
def fix_map(d :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, threshold_P_V :float, threshold_F_C :float, max_z_score :float) -> ET.ElementTree:
    """
    Edits the selected SVG map based on the p-value and fold change data (d) and some significance thresholds also passed as inputs.

    for el in core_map.iter():
        el_id = str(el.get('id'))
        if el_id.startswith('R_'):
            tmp = d.get(el_id[2:])
            if tmp != None:
                p_val :float = tmp[0]
                f_c = tmp[1]
                z_score = tmp[2]
                
                if math.isnan(p_val) or (isinstance(f_c, float) and math.isnan(f_c)): continue

                if p_val <= threshold_P_V:
                    if not isinstance(f_c, str):
                        if abs(f_c) < ((threshold_F_C - 1) / (abs(threshold_F_C) + 1)): # 
                            col = grey
                            width = str(minT)
                        else:
                            if f_c < 0:
                                col = blue
                            elif f_c > 0:
                                col = red
                            width = str(max((abs(z_score) * maxT) / max_z_score, minT))
                    else:
                        if f_c == '-INF':
                            col = blue
                        elif f_c == 'INF':
                            col = red
                        width = str(maxT)
                    dash = 'none'
                else:
                    dash = '5,5'
                    col = grey
                    width = str(minT)
                el.set('style', fix_style(el.get('style', ""), col, width, dash))
    return core_map

    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"

    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
    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)

        # 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 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:

# 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 applyRpsEnrichmentToMap(rpsEnrichmentRes :Dict[str, Union[Tuple[float, FoldChange], Tuple[float, FoldChange, float, float]]], metabMap :ET.ElementTree, maxNumericZScore :float) -> None:
    """
    Applies RPS enrichment results to the provided metabolic map.

    Args:

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

        if abs(foldChange) <  (ARGS.fChange - 1) / (abs(ARGS.fChange) + 1):
            INVALID_ARROW.styleReactionElements(metabMap, reactionId)
            continue
        
        width = Arrow.MAX_W
        if not math.isinf(foldChange):
            try: width = max(abs(z_score * Arrow.MAX_W) / maxNumericZScore, Arrow.MIN_W)
            except ZeroDivisionError: pass
        
        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 not ARGS.using_RAS: # style arrow body
            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:

        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
        

        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:

    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}')

            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, ras_enrichment: bool, outPath :utils.FilePath) -> None:
    fieldNames = ["ids", "P_Value", "fold change", "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", ras_enrichment = True) -> None:
    # this function compiles the things always in common between comparison modes after enrichment.
    # TODO: organize, name better.
    suffix = "RAS" if ras_enrichment else "RPS"
    details = f"Tabular Result ({suffix})"

    writeTabularResult(tmp, ras_enrichment, buildOutputPath(dataset1Name, dataset2Name, details = details, ext = utils.FileFormat.TSV))
    
    if ras_enrichment:
        fix_map(tmp, core_map, ARGS.pValue, ARGS.fChange, max_z_score)
        return

    
    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"
    tmp :Dict[str, List[Union[float, FoldChange]]] = {}
    count   = 0
    max_z_score = 0

    for l1, l2 in zip(dataset1Data, dataset2Data):
        reactId = ids[count]

                avg1 = sum(nets1)   / len(nets1)
                avg2 = sum(nets2)   / len(nets2)
                net = fold_change(avg1, avg2)
                
                if math.isnan(net): continue
                tmp[reactId[:-1] + "RV"] = [p_value, net, z_score, avg1, avg2]
                
                # vvv complementary directional ids are set to None once processed if net is to be applied to tips
                if ARGS.net:
                    ids[position] = None
                    continue

            # fallthrough is intended, regular scores need to be computed when tips aren't net but RAS datasets aren't used
            p_value, z_score = computePValue(l1, l2)
            avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2))
            if not isinstance(z_score, str) and max_z_score < abs(z_score): max_z_score = abs(z_score)
            tmp[reactId] = [float(p_value), avg, z_score, sum(l1) / len(l1), sum(l2) / len(l2)]
        
        except (TypeError, ZeroDivisionError): continue
    
    return tmp, max_z_score

def computeEnrichment(class_pat: Dict[str, List[List[float]]], ids: List[str], *, fromRAS=True) -> 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.

    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)")
        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

        sys.exit : if a user-provided custom map is in the wrong format (ET.XMLSyntaxError, ET.XMLSchemaParseError)
    """
    global ARGS
    ARGS = process_args(args)

    if not os.path.isdir(ARGS.output_path):
        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)
    
        # Prepare enrichment results containers
    ras_results = []
    rps_results = []

    # Compute RAS enrichment if requested
    if ARGS.using_RAS:

            ARGS.input_datas_rps, ARGS.input_data_rps, ARGS.input_class_rps, ARGS.names_rps)
        rps_results = computeEnrichment(class_pat_rps, ids_rps, fromRAS=False)

    # Organize by comparison pairs
    comparisons: Dict[Tuple[str, str], Dict[str, Tuple]] = {}
    for i, j, d, mz in ras_results:
        comparisons[(i, j)] = {'ras': (d, mz), 'rps': None}
    for i, j, d, mz in rps_results:
        comparisons.setdefault((i, j), {}).update({'rps': (d, mz)})

    # For each comparison, create a styled map with RAS bodies and RPS heads
    for (i, j), res in comparisons.items():
        map_copy = copy.deepcopy(core_map)

            temp_thingsInCommon(tmp_ras, map_copy, max_z_ras, i, j, ras_enrichment=True)

        # Apply RPS styling to arrow heads
        if res.get('rps'):
            tmp_rps, max_z_rps = res['rps']
            # Ensure applyRpsEnrichmentToMap styles only heads; adjust internals if needed
            temp_thingsInCommon(tmp_rps, map_copy, max_z_rps, i, j, ras_enrichment=False)

        # Output both SVG and PDF/PNG as configured
        createOutputMaps(i, j, map_copy)
    print('Execution succeeded')

286:eaa623b4b703

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

        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

############################ 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.

            "INF" : when avg2 is 0
            float : for any other combination of values
    """
    if avg1 == 0 and avg2 == 0:
        return 0
    
    if avg1 == 0:
        return '-INF' # TODO: maybe fix
    
    if avg2 == 0:
        return 'INF'
    
    # (threshold_F_C - 1) / (abs(threshold_F_C) + 1) con threshold_F_C > 1
    fc = (avg1 - avg2) / (abs(avg1) + abs(avg2))
    if avg1 < 0 and avg2 < 0: fc *= -1
    return fc

# TODO: I would really like for this one to get the Thanos treatment
def fix_style(l :str, col :Optional[str], width :str, dash :str) -> str:
    """
    Produces a "fixed" style string to assign to a reaction arrow in the SVG map, assigning style properties to the corresponding values passed as input params.

    Args:

        tmp.append('stroke-width:' + width)
    if not flag_dash:
        tmp.append('stroke-dasharray:' + dash)
    return ';'.join(tmp)

# TODO: remove, there's applyRPS whatever
# The type of d values is collapsed, losing precision, because the dict containst lists instead of tuples, please fix!
def fix_map(d :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, threshold_P_V :float, threshold_F_C :float, max_z_score :float) -> ET.ElementTree:
    """
    Edits the selected SVG map based on the p-value and fold change data (d) and some significance thresholds also passed as inputs.

    for el in core_map.iter():
        el_id = str(el.get('id'))
        if el_id.startswith('R_'):
            tmp = d.get(el_id[2:])
            if tmp != None:
                p_val, f_c, z_score, avg1, avg2 = tmp
                
                if math.isnan(p_val) or (isinstance(f_c, float) and math.isnan(f_c)): continue

                if p_val <= threshold_P_V: # p-value is OK
                    if not isinstance(f_c, str): # FC is finite
                        if abs(f_c) < ((threshold_F_C - 1) / (abs(threshold_F_C) + 1)): # FC is not OK
                            col = grey
                            width = str(minT)
                        else: # FC is OK
                            if f_c < 0:
                                col = blue
                            elif f_c > 0:
                                col = red
                            width = str(
                                min(
                                    max(abs(z_score * maxT) / max_z_score, minT),
                                    maxT))
                    
                    else: # FC is infinite
                        if f_c == '-INF':
                            col = blue
                        elif f_c == 'INF':
                            col = red
                        width = str(maxT)
                    dash = 'none'
                else: # p-value is not OK
                    dash = '5,5'
                    col = grey
                    width = str(minT)
                el.set('style', fix_style(el.get('style', ""), col, width, dash))
    return core_map

    Returns:
        utils.Result[ET.Element, ResultErr]: result of the search, either the first match found or a ResultErr.
    """
    return utils.Result.Ok(
        lambda: metabMap.xpath(f"//*[@id=\"{reactionId}\"]")[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]) # TODO: why the last 3? Are we sure?

    #TODO: this is attempting to solve the styling override problem, not sure it does tho

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

# TODO: maybe remove vvv
class ReactionDirection(Enum):
    Unknown = ""
    Direct  = "_F"
    Inverse = "_B"

    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], "" # ^^^ Invert _F to F_

    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
    UpRegulated   = "#ecac68" # orange, up-regulated reaction
    DownRegulated = "#6495ed" # lightblue, down-regulated reaction

    UpRegulatedInv = "#FF0000"
    # ^^^ bright red, up-regulated net value for a reversible reaction with
    # conflicting enrichment in the two directions.

    DownRegulatedInv = "#0000FF"
    # ^^^ bright blue, 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)

        # 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))
    
    # TODO: this seems to be unused, remove
    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:

# 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)

# TODO: A more general version of this can be used for RAS as well, we don't need "fix map" or whatever
def applyRpsEnrichmentToMap(rpsEnrichmentRes :Dict[str, Union[Tuple[float, FoldChange], Tuple[float, FoldChange, float, float]]], metabMap :ET.ElementTree, maxNumericZScore :float) -> None:
    """
    Applies RPS enrichment results to the provided metabolic map.

    Args:

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

        if abs(foldChange) < (ARGS.fChange - 1) / (abs(ARGS.fChange) + 1):
            INVALID_ARROW.styleReactionElements(metabMap, reactionId)
            continue
        
        width = Arrow.MAX_W
        if not math.isinf(foldChange):
            try: width = min(
                max(abs(z_score * Arrow.MAX_W) / maxNumericZScore, Arrow.MIN_W),
                Arrow.MAX_W)
            
            except ZeroDivisionError: pass
        
        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 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 not ARGS.using_RAS: # style arrow body
            arrow.styleReactionElements(metabMap, reactionId, mindReactionDir = False)

############################ split class ######################################
def split_class(classes :pd.DataFrame, dataset_values :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
        dataset_values : a :dict containing :float data

    Returns:
        dict : the dict with data grouped by class

    Side effects:

        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] # sample name
                values = dataset_values.get(pat_id, None) # the column of values for that sample
                if values != None:
                    l.append(values)
                classes.iloc[j, 1] = None # TODO: problems?
        
        if l:
            class_pat[classe] = list(map(list, zip(*l)))
            continue
        

        white_background = white_background.extract_band(0)

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

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:

    Returns:
        None
    """
    svg_to_png_with_background(file_svg, file_png)
    try:
        image = Image.open(file_png.show())
        image = image.convert("RGB")
        image.save(file_pdf.show(), "PDF", resolution=100.0)
        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}')

            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 temp_thingsInCommon(tmp :OldEnrichedScores, core_map :ET.ElementTree, max_z_score :float, dataset1Name :str, dataset2Name = "rest", ras_enrichment = True) -> None:
    # this function compiles the things always in common between comparison modes after enrichment.
    # TODO: organize, name better.
    suffix = "RAS" if ras_enrichment else "RPS"
    writeToCsv(
        [ [reactId] + values for reactId, values in tmp.items() ],
        ["ids", "P_Value", "fold change", "average_1", "average_2"],
        buildOutputPath(dataset1Name, dataset2Name, details = f"Tabular Result ({suffix})", ext = utils.FileFormat.TSV))
    
    if ras_enrichment:
        fix_map(tmp, core_map, ARGS.pValue, ARGS.fChange, max_z_score)
        return

    
    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]

                avg1 = sum(nets1)   / len(nets1)
                avg2 = sum(nets2)   / len(nets2)
                net = fold_change(avg1, avg2)
                
                if math.isnan(net): continue
                comparisonResult[reactId[:-1] + "RV"] = [p_value, net, z_score, avg1, avg2]
                
                # vvv complementary directional ids are set to None once processed if net is to be applied to tips
                if ARGS.net: # If only using RPS, we cannot delete the inverse, as it's needed to color the arrows
                    ids[position] = None
                    continue

            # fallthrough is intended, regular scores need to be computed when tips aren't net but RAS datasets aren't used
            p_value, z_score = computePValue(l1, l2)
            avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2))
            # vvv TODO: Check numpy version compatibility
            if np.isfinite(z_score) and max_z_score < abs(z_score): max_z_score = abs(z_score)
            comparisonResult[reactId] = [float(p_value), avg, z_score, sum(l1) / len(l1), sum(l2) / len(l2)]
        
        except (TypeError, ZeroDivisionError): continue
    
    return comparisonResult, max_z_score

def computeEnrichment(class_pat: Dict[str, List[List[float]]], ids: List[str], *, fromRAS=True) -> 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.

    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)
        svg_to_png_with_background(svgFilePath, pngPath)
        try:
            image = Image.open(pngPath.show())
            image = image.convert("RGB")
            image.save(pdfPath.show(), "PDF", resolution=100.0)
            print(f'PDF file {pdfPath.filePath} successfully generated.')
        
        except Exception as e:
            raise utils.DataErr(pdfPath.show(), f'Error generating PDF file: {e}')

    if not ARGS.generate_svg: # This argument is useless, who cares if the user wants the svg or not
        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
        for path, name in zip(datasetsPaths, names):
            name = str(name)
            if name == 'Dataset':
                name += '_' + str(num)
            
            values, ids = getDatasetValues(path, name)
            if values != None:
                class_pat[name] = list(map(list, zip(*values.values())))
                # TODO: ???
            
            num += 1
    
    elif ARGS.option == "dataset_class":
        classes = read_dataset(classPath, "class")
        classes = classes.astype(str)

        values, ids = getDatasetValues(datasetPath, "Dataset Class (not actual name)")
        if values != None: class_pat = split_class(classes, values)
    
    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

        sys.exit : if a user-provided custom map is in the wrong format (ET.XMLSyntaxError, ET.XMLSchemaParseError)
    """
    global ARGS
    ARGS = process_args(args)

    # Create output folder
    if not os.path.isdir(ARGS.output_path):
        os.makedirs(ARGS.output_path, exist_ok=True)
    
    core_map: ET.ElementTree = ARGS.choice_map.getMap(
        ARGS.tool_dir,
        utils.FilePath.fromStrPath(ARGS.custom_map) if ARGS.custom_map else None)
    
    # TODO: in the future keep the indices WITH the data and fix the code below.

    # Prepare enrichment results containers
    ras_results = []
    rps_results = []

    # Compute RAS enrichment if requested
    if ARGS.using_RAS:

            ARGS.input_datas_rps, ARGS.input_data_rps, ARGS.input_class_rps, ARGS.names_rps)
        rps_results = computeEnrichment(class_pat_rps, ids_rps, fromRAS=False)

    # Organize by comparison pairs
    comparisons: Dict[Tuple[str, str], Dict[str, Tuple]] = {}
    for i, j, comparison_data, max_z_score in ras_results:
        comparisons[(i, j)] = {'ras': (comparison_data, max_z_score), 'rps': None}
    for i, j, comparison_data, max_z_score in rps_results:
        comparisons.setdefault((i, j), {}).update({'rps': (comparison_data, max_z_score)})

    # For each comparison, create a styled map with RAS bodies and RPS heads
    for (i, j), res in comparisons.items():
        map_copy = copy.deepcopy(core_map)

            temp_thingsInCommon(tmp_ras, map_copy, max_z_ras, i, j, ras_enrichment=True)

        # Apply RPS styling to arrow heads
        if res.get('rps'):
            tmp_rps, max_z_rps = res['rps']
            # applyRpsEnrichmentToMap styles only heads unless only RPS are used
            temp_thingsInCommon(tmp_rps, map_copy, max_z_rps, i, j, ras_enrichment=False)

        # Output both SVG and PDF/PNG as configured
        createOutputMaps(i, j, map_copy)
    print('Execution succeeded')