cobraxy: COBRAxy/flux_to_map.py comparison

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

Uploaded

author	luca_milaz
date	Wed, 06 Nov 2024 20:57:11 +0000
parents	3fca9b568faf
children	8e3cbf68cdc4

comparison

equal deleted inserted replaced

-:3fca9b568faf
+: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]) -> None:
+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:
-None
+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):
-#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)
+enrichment_results.append((i, j, comparisonDict, max_z_score))
 elif ARGS.comparison == "onevsrest":
 for single_cluster in class_pat.keys():
-t :List[List[List[float]]] = []
+rest = [item for k, v in class_pat.items() if k != single_cluster for item in v]
-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)
+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
+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)
+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)
+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)
+pngPath = buildOutputPath(dataset1Name, dataset2Name, details="PNG Map", ext=utils.FileFormat.PNG)
-pdfPath = buildOutputPath(dataset1Name, dataset2Name, details = "PDF Map", ext = utils.FileFormat.PDF)
+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())
+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)
+enrichment_results = computeEnrichment(core_map, class_pat, ids)
+for i, j, comparisonDict, max_z_score in enrichment_results:
-# create output files: TODO: this is the same comparison happening in "maps", find a better way to organize this
+map_copy = copy.deepcopy(core_map)
-if ARGS.comparison == "manyvsmany":
+temp_thingsInCommon(comparisonDict, map_copy, max_z_score, i, j)
-for i, j in it.combinations(class_pat.keys(), 2): createOutputMaps(i, j, core_map)
+createOutputMaps(i, j, map_copy)
-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()

Mercurial > repos > bimib > cobraxy

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