Hochberg procedure integrated, update tutorial

confidenceMapUtil/FDRutil.py

@@ -446,7 +446,10 @@ def pAdjust(pValues, method):
 			pAdjust[i] = max(prevPVal, tmpPVal);
 			prevPVal = pAdjust[i];
 		pAdjust[pAdjust>1.0] = 1.0;
-		
+	elif method == "Hochberg":
+		for i in range(numPVal-1, -1, -1):
+			pAdjust[i] = min(prevPVal, pSort[i]*(numPVal-i));
+			prevPVal = pAdjust[i];		
 	else:
 		print('Please specify a method. Execution is stopped ...');
 		quit();	

confidenceMapUtil/confidenceMapMain.py

@@ -103,12 +103,12 @@ def calculateConfidenceMap(em_map, apix, noiseBox, testProc, ecdf, lowPassFilter
 
 		FDRutil.checkNormality(em_map, wn, boxCoord);
 		em_map, mean, var, ECDF = mapUtil.localFiltration(em_map, locResMap, apix, True, wn, boxCoord, ECDF);
-		#em_map = FDRutil.studentizeMap(em_map, mean, var);
+		#locFiltMap = FDRutil.studentizeMap(em_map, mean, var);
 		locFiltMap = em_map;
 		locScaleMap = None;
 	else:
 		em_map, mean, var, ECDF = locscaleUtil.launch_amplitude_scaling(em_map, modelMap, apix, stepSize, wn_locscale, wn, method, locResMap, boxCoord, mpi, ECDF );
-		#em_map = FDRutil.studentizeMap(em_map, mean, var);
+		#locScaleMap = FDRutil.studentizeMap(em_map, mean, var);
 		locScaleMap = em_map;
 		locFiltMap = None;
 
@@ -137,10 +137,6 @@ def calculateConfidenceMap(em_map, apix, noiseBox, testProc, ecdf, lowPassFilter
 		fdr = 0.01;
 		binMap = FDRutil.binarizeMap(qMap, fdr);
 
-		# apply the thresholded qMap to data
-		maskedMap = np.multiply(binMap, np.copy(em_map));
-		minMapValue = np.min(maskedMap[np.nonzero(maskedMap)]);
-
 		if (locResMap is None) & (modelMap is None):  # if no local Resolution map is give, then give the correspoding threshold, not usefule with local filtration
 			# apply the thresholded qMap to data
                 	maskedMap = np.multiply(binMap, np.copy(em_map));
@@ -148,19 +144,18 @@ def calculateConfidenceMap(em_map, apix, noiseBox, testProc, ecdf, lowPassFilter
 			output = "Calculated map threshold: " + repr(minMapValue) + " at a FDR of " + repr(fdr*100) + "%.";
 			print(output);
 		"""elif (locResMap is not None) & (modelMap is None):
-                        # apply the thresholded qMap to data
-                        maskedMap = np.multiply(binMap, np.copy(locFiltMap));
-                        minMapValue = np.min(maskedMap[np.nonzero(maskedMap)]);
-                        output = "Calculated map threshold: " + repr(minMapValue) + " at a FDR of " + repr(fdr*100) + "%.";
-                        print(output);
+			# apply the thresholded qMap to data
+			maskedMap = np.multiply(binMap, np.copy(locFiltMap));
+			minMapValue = np.min(maskedMap[np.nonzero(maskedMap)]);
+			output = "Calculated map threshold: " + repr(minMapValue) + " at a FDR of " + repr(fdr*100) + "%.";
+			print(output);
 		elif (locResMap is None) & (modelMap is not None):
-                        # apply the thresholded qMap to data
-                        maskedMap = np.multiply(binMap, np.copy(locScaleMap));
-                        minMapValue = np.min(maskedMap[np.nonzero(maskedMap)]);
-                        output = "Calculated map threshold: " + repr(minMapValue) + " at a FDR of " + repr(fdr*100) + "%.";
-                        print(output);
+			# apply the thresholded qMap to data
+			maskedMap = np.multiply(binMap, np.copy(locScaleMap));
+			minMapValue = np.min(maskedMap[np.nonzero(maskedMap)]);
+			output = "Calculated map threshold: " + repr(minMapValue) + " at a FDR of " + repr(fdr*100) + "%.";
+			print(output);
 		"""	
-
 		binMap = None;
 		maskedMap = None;