comparison COBRAxy/flux_to_map.py @ 148:5683406a8cfd draft

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147:3fca9b568faf

            tmp[reactId] = [float(p_value), avg, z_score, avg1, avg2]
        except (TypeError, ZeroDivisionError): continue
    
    return tmp, max_z_score

def computeEnrichment(metabMap :ET.ElementTree, class_pat :Dict[str, List[List[float]]], ids :List[str]) -> None:
    """
    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:
        None

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

    """
    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')

    if ARGS.comparison == "manyvsmany":
        for i, j in it.combinations(class_pat.keys(), 2):
            #TODO: these 2 functions are always called in pair and in this order and need common data,
            # some clever refactoring would be appreciated.
            comparisonDict, max_z_score = compareDatasetPair(class_pat.get(i), class_pat.get(j), ids)
            temp_thingsInCommon(comparisonDict, metabMap, max_z_score, i, j)
    
    elif ARGS.comparison == "onevsrest":
        for single_cluster in class_pat.keys():
            t :List[List[List[float]]] = []
            for k in class_pat.keys():
                if k != single_cluster:
                   t.append(class_pat.get(k))
            
            rest :List[List[float]] = []
            for i in t:
                rest = rest + i
            
            comparisonDict, max_z_score = compareDatasetPair(class_pat.get(single_cluster), rest, ids)
            temp_thingsInCommon(comparisonDict, metabMap, max_z_score, single_cluster)
    
    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)
            temp_thingsInCommon(comparisonDict, metabMap, max_z_score, ARGS.control, otherDataset)

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.

        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.

    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)
    

    computeEnrichment(core_map, class_pat, ids)
    
    # create output files: TODO: this is the same comparison happening in "maps", find a better way to organize this
    if ARGS.comparison == "manyvsmany":
        for i, j in it.combinations(class_pat.keys(), 2): createOutputMaps(i, j, core_map)
        return
    
    if ARGS.comparison == "onevsrest":
        for single_cluster in class_pat.keys(): createOutputMaps(single_cluster, "rest", core_map)
        return
    
    for otherDataset in class_pat.keys():
        if otherDataset != ARGS.control: createOutputMaps(i, j, core_map)

    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()

148:5683406a8cfd

            tmp[reactId] = [float(p_value), avg, z_score, avg1, avg2]
        except (TypeError, ZeroDivisionError): continue
    
    return tmp, max_z_score

def computeEnrichment(metabMap :ET.ElementTree, 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
    
    Side effects:

    """
    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))
    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.

        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.

    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(core_map, 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()