comparison COBRAxy/flux_to_map.py @ 168:fa981714e87d draft

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167:eefe693b2fdd

    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 normalized across classes/datasets and visualized 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.

    metabMap_mean = copy.deepcopy(metabMap)
    metabMap_median = copy.deepcopy(metabMap)

    # Compute medians and means
    medians = {key: np.round(np.median(np.array(value), axis=1), 6) for key, value in class_pat.items()}
    means = {key: np.round(np.mean(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(np.min(arr) for arr in medians.values())
    min_flux_means = min(np.min(arr) for arr in means.values())

    max_flux_medians = max(np.max(arr) for arr in medians.values())
    max_flux_means = max(np.max(arr) for arr in means.values())

    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)

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

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

        for i, rxn_id in enumerate(ids):
            isNegative = medians[key][i] < 0

168:fa981714e87d

    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.

    metabMap_mean = copy.deepcopy(metabMap)
    metabMap_median = copy.deepcopy(metabMap)

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

    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):
            isNegative = medians[key][i] < 0