diff --git a/schainpy/model/io/jroIO_param.py b/schainpy/model/io/jroIO_param.py
index baf24d1..6f5dfd5 100644
--- a/schainpy/model/io/jroIO_param.py
+++ b/schainpy/model/io/jroIO_param.py
@@ -399,7 +399,70 @@ class ParamReader(ProcessingUnit):
         self.listData = listdata
         return
     
+    def __setDataArray(self, dataset, shapes):
+                
+        nDims = shapes[0]        
+                
+        nDim2 = shapes[1]      #Dimension 0
     
+        nDim1 = shapes[2]      #Dimension 1, number of Points or Parameters
+    
+        nDim0 = shapes[3]      #Dimension 2, number of samples or ranges
+        
+        mode = shapes[4]        #Mode of storing
+        
+        blockList = self.blockList
+        
+        blocksPerFile = self.blocksPerFile
+        
+        #Depending on what mode the data was stored
+        if mode == 0:       #Divided in channels
+            arrayData = dataset.value.astype(numpy.float)[0][blockList]
+        if mode == 1:     #Divided in parameter
+            strds = 'table'
+            nDatas = nDim1
+            newShapes = (blocksPerFile,nDim2,nDim0)
+        elif mode==2:       #Concatenated in a table
+            strds = 'table0'
+            arrayData = dataset[strds].value
+            #Selecting part of the dataset
+            utctime = arrayData[:,0]
+            u, indices = numpy.unique(utctime, return_index=True)
+            
+            if blockList.size != indices.size:
+                indMin = indices[blockList[0]]
+                if blockList[1] + 1 >= indices.size:
+                    arrayData = arrayData[indMin:,:]
+                else:
+                    indMax = indices[blockList[1] + 1]
+                    arrayData = arrayData[indMin:indMax,:]
+            return arrayData
+        
+        #    One dimension 
+        if nDims == 0:
+            arrayData = dataset.value.astype(numpy.float)[0][blockList]
+                        
+        #    Two dimensions    
+        elif nDims == 2:
+            arrayData = numpy.zeros((blocksPerFile,nDim1,nDim0))
+            newShapes = (blocksPerFile,nDim0)
+            nDatas = nDim1    
+             
+            for i in range(nDatas): 
+                data = dataset[strds + str(i)].value
+                arrayData[:,i,:] = data[blockList,:]    
+                
+        #    Three dimensions    
+        else:
+            arrayData = numpy.zeros((blocksPerFile,nDim2,nDim1,nDim0))               
+            for i in range(nDatas):
+                
+                data = dataset[strds + str(i)].value
+                
+                for b in range(blockList.size):
+                    arrayData[b,:,i,:] = data[:,:,blockList[b]]
+                
+        return arrayData    
         
     def __setDataOut(self):
         listMeta = self.listMeta