Mercurial > repos > bimib > cobraxy

comparison COBRAxy/flux_to_map.py @ 240:63f5078627a9 draft

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Uploaded
author francesco_lapi
date Mon, 13 Jan 2025 10:01:40 +0000
parents 5c70439f2907
children 049aa0f4844f
comparison
equal deleted inserted replaced
239:7bd33d296319 240:63f5078627a9
829 829
830 Returns: 830 Returns:
831 Tuple[ClassPat, List[str]]: values and IDs extracted from the dataset 831 Tuple[ClassPat, List[str]]: values and IDs extracted from the dataset
832 """ 832 """
833 dataset = read_dataset(datasetPath, datasetName) 833 dataset = read_dataset(datasetPath, datasetName)
834
835 # Ensure the first column is treated as the reaction name
836 dataset = dataset.set_index(dataset.columns[0])
837
838 # Check if required reactions exist in the dataset
839 required_reactions = ['EX_lac__L_e', 'EX_glc__D_e', 'EX_gln__L_e', 'EX_glu__L_e']
840 missing_reactions = [reaction for reaction in required_reactions if reaction not in dataset.index]
841
842 if missing_reactions:
843 sys.exit(f'Execution aborted: Missing required reactions {missing_reactions} in {datasetName}\n')
844
845 # Calculate new rows using safe division
846 lact_glc = np.divide(
847 dataset.loc['EX_lac__L_e'], -dataset.loc['EX_glc__D_e'],
848 out=np.zeros_like(dataset.loc['EX_lac__L_e']), where=dataset.loc['EX_glc__D_e'] != 0
849 )
850 lact_gln = np.divide(
851 dataset.loc['EX_lac__L_e'], -dataset.loc['EX_gln__L_e'],
852 out=np.zeros_like(dataset.loc['EX_lac__L_e']), where=dataset.loc['EX_gln__L_e'] != 0
853 )
854 glu_gln = np.divide(
855 dataset.loc['EX_glu__L_e'], -dataset.loc['EX_gln__L_e'],
856 out=np.zeros_like(dataset.loc['EX_glu__L_e']), where=dataset.loc['EX_gln__L_e'] != 0
857 )
858
859 # Create a DataFrame for the new rows
860 new_rows = pd.DataFrame({
861 dataset.index.name: ['LactGlc', 'LactGln', 'GluGln'],
862 **{col: [lact_glc[i], lact_gln[i], glu_gln[i]] for i, col in enumerate(dataset.columns)}
863 })
864
865 # Reset the index of the original dataset and append new rows
866 dataset.reset_index(inplace=True)
867 dataset = pd.concat([dataset, new_rows], ignore_index=True)
868
834 IDs = pd.Series.tolist(dataset.iloc[:, 0].astype(str)) 869 IDs = pd.Series.tolist(dataset.iloc[:, 0].astype(str))
835 870
836 dataset = dataset.drop(dataset.columns[0], axis = "columns").to_dict("list") 871 dataset = dataset.drop(dataset.columns[0], axis = "columns").to_dict("list")
837 return { id : list(map(utils.Float("Dataset values, not an argument"), values)) for id, values in dataset.items() }, IDs 872 return { id : list(map(utils.Float("Dataset values, not an argument"), values)) for id, values in dataset.items() }, IDs
838 873
925 960
926 save_colormap_image(min_flux_medians, max_flux_medians, utils.FilePath("Color map median", ext=utils.FileFormat.PNG, prefix=ARGS.output_path), colormap) 961 save_colormap_image(min_flux_medians, max_flux_medians, utils.FilePath("Color map median", ext=utils.FileFormat.PNG, prefix=ARGS.output_path), colormap)
927 save_colormap_image(min_flux_means, max_flux_means, utils.FilePath("Color map mean", ext=utils.FileFormat.PNG, prefix=ARGS.output_path), colormap) 962 save_colormap_image(min_flux_means, max_flux_means, utils.FilePath("Color map mean", ext=utils.FileFormat.PNG, prefix=ARGS.output_path), colormap)
928 963
929 cmap = plt.get_cmap(colormap) 964 cmap = plt.get_cmap(colormap)
965
966 min_width = 2.0 # Minimum arrow width
967 max_width = 15.0 # Maximum arrow width
930 968
931 for key in class_pat: 969 for key in class_pat:
932 # Create color mappings for median and mean 970 # Create color mappings for median and mean
933 colors_median = { 971 colors_median = {
934 rxn_id: rgb_to_hex(cmap(abs(medians[key][i]))) if medians[key][i] != 0 else '#bebebe' #grey blocked 972 rxn_id: rgb_to_hex(cmap(abs(medians[key][i]))) if medians[key][i] != 0 else '#bebebe' #grey blocked
939 rxn_id: rgb_to_hex(cmap(abs(means[key][i]))) if means[key][i] != 0 else '#bebebe' #grey blocked 977 rxn_id: rgb_to_hex(cmap(abs(means[key][i]))) if means[key][i] != 0 else '#bebebe' #grey blocked
940 for i, rxn_id in enumerate(ids) 978 for i, rxn_id in enumerate(ids)
941 } 979 }
942 980
943 for i, rxn_id in enumerate(ids): 981 for i, rxn_id in enumerate(ids):
982 # Calculate arrow width for median
983 width_median = np.interp(abs(medians[key][i]), [0, 1], [min_width, max_width])
944 isNegative = medians[key][i] < 0 984 isNegative = medians[key][i] < 0
945 985 apply_arrow(metabMap_median, rxn_id, colors_median[rxn_id], isNegative, width_median)
946 # Apply median arrows 986
947 apply_arrow(metabMap_median, rxn_id, colors_median[rxn_id], isNegative) 987 # Calculate arrow width for mean
948 988 width_mean = np.interp(abs(means[key][i]), [0, 1], [min_width, max_width])
949 isNegative = means[key][i] < 0 989 isNegative = means[key][i] < 0
950 # Apply mean arrows 990 apply_arrow(metabMap_mean, rxn_id, colors_mean[rxn_id], isNegative, width_mean)
951 apply_arrow(metabMap_mean, rxn_id, colors_mean[rxn_id], isNegative)
952 991
953 # Save and convert the SVG files 992 # Save and convert the SVG files
954 save_and_convert(metabMap_mean, "mean", key) 993 save_and_convert(metabMap_mean, "mean", key)
955 save_and_convert(metabMap_median, "median", key) 994 save_and_convert(metabMap_median, "median", key)
956 995
957 def apply_arrow(metabMap, rxn_id, color, isNegative): 996 def apply_arrow(metabMap, rxn_id, color, isNegative, width=5):
958 """ 997 """
959 Apply an arrow to a specific reaction in the metabolic map with a given color. 998 Apply an arrow to a specific reaction in the metabolic map with a given color.
960 999
961 Args: 1000 Args:
962 metabMap (ET.ElementTree): An XML tree representing the metabolic map. 1001 metabMap (ET.ElementTree): An XML tree representing the metabolic map.
963 rxn_id (str): The ID of the reaction to which the arrow will be applied. 1002 rxn_id (str): The ID of the reaction to which the arrow will be applied.
964 color (str): The color of the arrow in hexadecimal format. 1003 color (str): The color of the arrow in hexadecimal format.
1004 isNegative (bool): A boolean indicating if the arrow represents a negative value.
1005 width (int): The width of the arrow.
965 1006
966 Returns: 1007 Returns:
967 None 1008 None
968 """ 1009 """
969 arrow = Arrow(width=5, col=color) 1010 arrow = Arrow(width=width, col=color)
970 arrow.styleReactionElementsMeanMedian(metabMap, rxn_id, isNegative) 1011 arrow.styleReactionElementsMeanMedian(metabMap, rxn_id, isNegative)
971 pass 1012 pass
972 1013
973 def save_and_convert(metabMap, map_type, key): 1014 def save_and_convert(metabMap, map_type, key):
974 """ 1015 """
1005 """ 1046 """
1006 1047
1007 global ARGS 1048 global ARGS
1008 ARGS = process_args(args) 1049 ARGS = process_args(args)
1009 1050
1051 if ARGS.custom_map == 'None':
1052 ARGS.custom_map = None
1053
1010 if os.path.isdir(ARGS.output_path) == False: os.makedirs(ARGS.output_path) 1054 if os.path.isdir(ARGS.output_path) == False: os.makedirs(ARGS.output_path)
1011 1055
1012 core_map :ET.ElementTree = ARGS.choice_map.getMap( 1056 core_map :ET.ElementTree = ARGS.choice_map.getMap(
1013 ARGS.tool_dir, 1057 ARGS.tool_dir,
1014 utils.FilePath.fromStrPath(ARGS.custom_map) if ARGS.custom_map else None) 1058 utils.FilePath.fromStrPath(ARGS.custom_map) if ARGS.custom_map else None)