diff --git a/geos-mesh/src/geos/mesh/utils/arrayHelpers.py b/geos-mesh/src/geos/mesh/utils/arrayHelpers.py
index 3139d67f..4498203f 100644
--- a/geos-mesh/src/geos/mesh/utils/arrayHelpers.py
+++ b/geos-mesh/src/geos/mesh/utils/arrayHelpers.py
@@ -7,9 +7,9 @@
 import numpy.typing as npt
 import pandas as pd  # type: ignore[import-untyped]
 import vtkmodules.util.numpy_support as vnp
-from typing import Optional, Union, cast
+from typing import Optional, Union, Any, cast
 from vtkmodules.util.numpy_support import vtk_to_numpy
-from vtkmodules.vtkCommonCore import vtkDataArray, vtkDoubleArray, vtkPoints
+from vtkmodules.vtkCommonCore import vtkDataArray, vtkPoints
 from vtkmodules.vtkCommonDataModel import ( vtkUnstructuredGrid, vtkFieldData, vtkMultiBlockDataSet, vtkDataSet,
                                             vtkCompositeDataSet, vtkDataObject, vtkPointData, vtkCellData,
                                             vtkDataObjectTreeIterator, vtkPolyData )
@@ -343,7 +343,7 @@ def isAttributeInObjectDataSet( object: vtkDataSet, attributeName: str, onPoints
     return bool( data.HasArray( attributeName ) )
 
 
-def getArrayInObject( object: vtkDataSet, attributeName: str, onPoints: bool ) -> npt.NDArray[ np.float64 ]:
+def getArrayInObject( object: vtkDataSet, attributeName: str, onPoints: bool ) -> npt.NDArray[ Any ]:
     """Return the numpy array corresponding to input attribute name in table.
 
     Args:
@@ -355,12 +355,51 @@ def getArrayInObject( object: vtkDataSet, attributeName: str, onPoints: bool ) -
     Returns:
         ArrayLike[float]: the array corresponding to input attribute name.
     """
-    array: vtkDoubleArray = getVtkArrayInObject( object, attributeName, onPoints )
-    nparray: npt.NDArray[ np.float64 ] = vnp.vtk_to_numpy( array )  # type: ignore[no-untyped-call]
+    array: vtkDataArray = getVtkArrayInObject( object, attributeName, onPoints )
+    nparray: npt.NDArray[ Any ] = vnp.vtk_to_numpy( array )  # type: ignore[no-untyped-call]
     return nparray
 
 
-def getVtkArrayInObject( object: vtkDataSet, attributeName: str, onPoints: bool ) -> vtkDoubleArray:
+def getVtkArrayTypeInObject( object: vtkDataSet, attributeName: str, onPoints: bool ) -> int:
+    """Return the type of the vtk array corrsponding to input attribute name in table.
+
+    Args:
+        object (PointSet or UnstructuredGrid): input object.
+        attributeName (str): name of the attribute.
+        onPoints (bool): True if attributes are on points, False if they are on cells.
+
+    Returns:
+        int: the type of the vtk array corrsponding to input attribute name.
+    """
+    array: vtkDataArray = getVtkArrayInObject( object, attributeName, onPoints )
+    vtkArrayType: int = array.GetDataType()
+
+    return vtkArrayType
+
+
+def getVtkArrayTypeInMultiBlock( multiBlockDataSet: vtkMultiBlockDataSet, attributeName: str, onPoints: bool ) -> int:
+    """Return the type of the vtk array corrsponding to input attribute name in the multiblock data set if it exist.
+
+    Args:
+        multiBlockDataSet (PointSet or UnstructuredGrid): input object.
+        attributeName (str): name of the attribute.
+        onPoints (bool): True if attributes are on points, False if they are on cells.
+
+    Returns:
+        int: type of the vtk array corrsponding to input attribute name, -1 if the multiblock has no attribute with given name.
+    """
+    nbBlocks = multiBlockDataSet.GetNumberOfBlocks()
+    for idBlock in range( nbBlocks ):
+        object: vtkDataSet = cast( vtkDataSet, multiBlockDataSet.GetBlock( idBlock ) )
+        listAttributes: set[ str ] = getAttributeSet( object, onPoints )
+        if attributeName in listAttributes:
+            return getVtkArrayTypeInObject( object, attributeName, onPoints )
+
+    print( "The vtkMultiBlockDataSet has no attribute with the name " + attributeName + "." )
+    return -1
+
+
+def getVtkArrayInObject( object: vtkDataSet, attributeName: str, onPoints: bool ) -> vtkDataArray:
     """Return the array corresponding to input attribute name in table.
 
     Args:
@@ -370,7 +409,7 @@ def getVtkArrayInObject( object: vtkDataSet, attributeName: str, onPoints: bool
             on cells.
 
     Returns:
-        vtkDoubleArray: the vtk array corresponding to input attribute name.
+        vtkDataArray: the vtk array corresponding to input attribute name.
     """
     assert isAttributeInObject( object, attributeName, onPoints ), f"{attributeName} is not in input object."
     return object.GetPointData().GetArray( attributeName ) if onPoints else object.GetCellData().GetArray(
@@ -414,7 +453,7 @@ def getNumberOfComponentsDataSet( dataSet: vtkDataSet, attributeName: str, onPoi
     Returns:
         int: number of components.
     """
-    array: vtkDoubleArray = getVtkArrayInObject( dataSet, attributeName, onPoints )
+    array: vtkDataArray = getVtkArrayInObject( dataSet, attributeName, onPoints )
     return array.GetNumberOfComponents()
 
 
@@ -438,7 +477,7 @@ def getNumberOfComponentsMultiBlock(
     for blockIndex in elementaryBlockIndexes:
         block: vtkDataSet = cast( vtkDataSet, getBlockFromFlatIndex( dataSet, blockIndex ) )
         if isAttributeInObject( block, attributeName, onPoints ):
-            array: vtkDoubleArray = getVtkArrayInObject( block, attributeName, onPoints )
+            array: vtkDataArray = getVtkArrayInObject( block, attributeName, onPoints )
             return array.GetNumberOfComponents()
     return 0
 
@@ -482,7 +521,7 @@ def getComponentNamesDataSet( dataSet: vtkDataSet, attributeName: str, onPoints:
         tuple[str,...]: names of the components.
 
     """
-    array: vtkDoubleArray = getVtkArrayInObject( dataSet, attributeName, onPoints )
+    array: vtkDataArray = getVtkArrayInObject( dataSet, attributeName, onPoints )
     componentNames: list[ str ] = []
 
     if array.GetNumberOfComponents() > 1:
diff --git a/geos-mesh/src/geos/mesh/utils/arrayModifiers.py b/geos-mesh/src/geos/mesh/utils/arrayModifiers.py
index 6d9a738c..df530189 100644
--- a/geos-mesh/src/geos/mesh/utils/arrayModifiers.py
+++ b/geos-mesh/src/geos/mesh/utils/arrayModifiers.py
@@ -1,24 +1,29 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright 2023-2024 TotalEnergies.
-# SPDX-FileContributor: Martin Lemay, Alexandre Benedicto, Paloma Martinez
+# SPDX-FileContributor: Martin Lemay, Alexandre Benedicto, Paloma Martinez, Romain Baville
 import numpy as np
 import numpy.typing as npt
 import vtkmodules.util.numpy_support as vnp
-from typing import Union
-from vtkmodules.vtkCommonDataModel import ( vtkMultiBlockDataSet, vtkDataSet, vtkPointSet, vtkCompositeDataSet,
-                                            vtkDataObject, vtkDataObjectTreeIterator )
-from vtkmodules.vtkFiltersCore import vtkArrayRename, vtkCellCenters, vtkPointDataToCellData
+from typing import Union, Any
 from vtk import (  # type: ignore[import-untyped]
-    VTK_CHAR, VTK_DOUBLE, VTK_FLOAT, VTK_INT, VTK_UNSIGNED_INT,
+    VTK_DOUBLE, VTK_FLOAT,
+)
+from vtkmodules.vtkCommonDataModel import (
+    vtkMultiBlockDataSet,
+    vtkDataSet,
+    vtkPointSet,
+    vtkCompositeDataSet,
+    vtkDataObject,
+    vtkDataObjectTreeIterator,
+)
+from vtkmodules.vtkFiltersCore import (
+    vtkArrayRename,
+    vtkCellCenters,
+    vtkPointDataToCellData,
 )
 from vtkmodules.vtkCommonCore import (
-    vtkCharArray,
     vtkDataArray,
-    vtkDoubleArray,
-    vtkFloatArray,
-    vtkIntArray,
     vtkPoints,
-    vtkUnsignedIntArray,
 )
 from geos.mesh.utils.arrayHelpers import (
     getComponentNames,
@@ -26,8 +31,13 @@
     getAttributeSet,
     getArrayInObject,
     isAttributeInObject,
+    getVtkArrayTypeInObject,
+    getVtkArrayTypeInMultiBlock,
+)
+from geos.mesh.utils.multiblockHelpers import (
+    getBlockElementIndexesFlatten,
+    getBlockFromFlatIndex,
 )
-from geos.mesh.utils.multiblockHelpers import getBlockElementIndexesFlatten, getBlockFromFlatIndex
 
 __doc__ = """
 ArrayModifiers contains utilities to process VTK Arrays objects.
@@ -40,154 +50,186 @@
 
 
 def fillPartialAttributes(
-    multiBlockMesh: Union[ vtkMultiBlockDataSet, vtkCompositeDataSet, vtkDataObject ],
+    multiBlockDataSet: Union[ vtkMultiBlockDataSet, vtkCompositeDataSet, vtkDataObject ],
     attributeName: str,
-    nbComponents: int,
     onPoints: bool = False,
+    value: Any = np.nan,
 ) -> bool:
-    """Fill input partial attribute of multiBlockMesh with nan values.
+    """Fill input partial attribute of multiBlockDataSet with the same value for all the components.
 
     Args:
-        multiBlockMesh (vtkMultiBlockDataSet | vtkCompositeDataSet | vtkDataObject): multiBlock
-            mesh where to fill the attribute
-        attributeName (str): attribute name
-        nbComponents (int): number of components
-        onPoints (bool, optional): Attribute is on Points (False) or
-            on Cells.
-
+        multiBlockDataSet (vtkMultiBlockDataSet | vtkCompositeDataSet | vtkDataObject): multiBlockDataSet where to fill the attribute.
+        attributeName (str): attribute name.
+        onPoints (bool, optional): Attribute is on Points (True) or on Cells (False).
             Defaults to False.
+        value (any, optional): value to fill in the partial atribute.
+            Defaults to nan. For int vtk array, default value is automatically set to -1.
 
     Returns:
-        bool: True if calculation successfully ended, False otherwise
+        bool: True if calculation successfully ended.
     """
+    vtkArrayType: int = getVtkArrayTypeInMultiBlock( multiBlockDataSet, attributeName, onPoints )
+    assert vtkArrayType != -1
+
+    infoAttributes: dict[ str, int ] = getAttributesWithNumberOfComponents( multiBlockDataSet, onPoints )
+    nbComponents: int = infoAttributes[ attributeName ]
+
     componentNames: tuple[ str, ...] = ()
     if nbComponents > 1:
-        componentNames = getComponentNames( multiBlockMesh, attributeName, onPoints )
-    values: list[ float ] = [ np.nan for _ in range( nbComponents ) ]
-    createConstantAttribute( multiBlockMesh, values, attributeName, componentNames, onPoints )
-    multiBlockMesh.Modified()
+        componentNames = getComponentNames( multiBlockDataSet, attributeName, onPoints )
+
+    valueType: Any = type( value )
+    typeMapping: dict[ int, Any ] = vnp.get_vtk_to_numpy_typemap()
+    valueTypeExpected: Any = typeMapping[ vtkArrayType ]
+    if valueTypeExpected != valueType:
+        if np.isnan( value ):
+            if vtkArrayType in ( VTK_DOUBLE, VTK_FLOAT ):
+                value = valueTypeExpected( value )
+            else:
+                print( attributeName + " vtk array type is " + str( valueTypeExpected ) +
+                       ", default value is automatically set to -1." )
+                value = valueTypeExpected( -1 )
+
+        else:
+            print( "The value has the wrong type, it is update to " + str( valueTypeExpected ) + ", the type of the " +
+                   attributeName + " array to fill." )
+            value = valueTypeExpected( value )
+
+    values: list[ Any ] = [ value for _ in range( nbComponents ) ]
+
+    createConstantAttribute( multiBlockDataSet, values, attributeName, componentNames, onPoints, vtkArrayType )
+    multiBlockDataSet.Modified()
+
     return True
 
 
 def fillAllPartialAttributes(
-    multiBlockMesh: Union[ vtkMultiBlockDataSet, vtkCompositeDataSet, vtkDataObject ],
-    onPoints: bool = False,
+    multiBlockDataSet: Union[ vtkMultiBlockDataSet, vtkCompositeDataSet, vtkDataObject ],
+    value: Any = np.nan,
 ) -> bool:
-    """Fill all the partial attributes of multiBlockMesh with nan values.
+    """Fill all the partial attributes of multiBlockDataSet with same value for all attributes and they components.
 
     Args:
-        multiBlockMesh (vtkMultiBlockDataSet | vtkCompositeDataSet | vtkDataObject):
-            multiBlockMesh where to fill the attribute
-        onPoints (bool, optional): Attribute is on Points (False) or
-            on Cells.
-
-            Defaults to False.
+        multiBlockDataSet (vtkMultiBlockDataSet | vtkCompositeDataSet | vtkDataObject): multiBlockDataSet where to fill the attribute.
+        value (any, optional): value to fill in the partial atribute.
+            Defaults to nan. For int vtk array, default value is automatically set to -1.
 
     Returns:
-        bool: True if calculation successfully ended, False otherwise
+        bool: True if calculation successfully ended.
     """
-    attributes: dict[ str, int ] = getAttributesWithNumberOfComponents( multiBlockMesh, onPoints )
-    for attributeName, nbComponents in attributes.items():
-        fillPartialAttributes( multiBlockMesh, attributeName, nbComponents, onPoints )
-    multiBlockMesh.Modified()
+    for onPoints in [ True, False ]:
+        infoAttributes: dict[ str, int ] = getAttributesWithNumberOfComponents( multiBlockDataSet, onPoints )
+        for attributeName in infoAttributes:
+            fillPartialAttributes( multiBlockDataSet, attributeName, onPoints, value )
+
+    multiBlockDataSet.Modified()
+
     return True
 
 
 def createEmptyAttribute(
     attributeName: str,
     componentNames: tuple[ str, ...],
-    dataType: int,
+    vtkDataType: int,
 ) -> vtkDataArray:
     """Create an empty attribute.
 
     Args:
         attributeName (str): name of the attribute
-        componentNames (tuple[str,...]): name of the components for vectorial
-            attributes
-        dataType (int): data type.
+        componentNames (tuple[str,...]): name of the components for vectorial attributes.
+        vtkDataType (int): data type.
 
     Returns:
-        bool: True if the attribute was correctly created
+        bool: True if the attribute was correctly created.
     """
-    # create empty array
-    newAttr: vtkDataArray
-    if dataType == VTK_DOUBLE:
-        newAttr = vtkDoubleArray()
-    elif dataType == VTK_FLOAT:
-        newAttr = vtkFloatArray()
-    elif dataType == VTK_INT:
-        newAttr = vtkIntArray()
-    elif dataType == VTK_UNSIGNED_INT:
-        newAttr = vtkUnsignedIntArray()
-    elif dataType == VTK_CHAR:
-        newAttr = vtkCharArray()
-    else:
+    vtkDataTypeOk: dict = vnp.get_vtk_to_numpy_typemap()
+    if vtkDataType not in vtkDataTypeOk:
         raise ValueError( "Attribute type is unknown." )
 
-    newAttr.SetName( attributeName )
-    newAttr.SetNumberOfComponents( len( componentNames ) )
-    if len( componentNames ) > 1:
-        for i in range( len( componentNames ) ):
-            newAttr.SetComponentName( i, componentNames[ i ] )
+    nbComponents: int = len( componentNames )
 
-    return newAttr
+    createdAttribute: vtkDataArray = vtkDataArray.CreateDataArray( vtkDataType )
+    createdAttribute.SetName( attributeName )
+    createdAttribute.SetNumberOfComponents( nbComponents )
+    if nbComponents > 1:
+        for i in range( nbComponents ):
+            createdAttribute.SetComponentName( i, componentNames[ i ] )
+
+    return createdAttribute
 
 
 def createConstantAttribute(
     object: Union[ vtkMultiBlockDataSet, vtkCompositeDataSet, vtkDataObject ],
     values: list[ float ],
     attributeName: str,
-    componentNames: tuple[ str, ...],
-    onPoints: bool,
+    componentNames: tuple[ str, ...] = (),  # noqa: C408
+    onPoints: bool = False,
+    vtkDataType: Union[ int, Any ] = None,
 ) -> bool:
     """Create an attribute with a constant value everywhere if absent.
 
     Args:
-        object (vtkDataObject): object (vtkMultiBlockDataSet, vtkDataSet)
-            where to create the attribute
-        values ( list[float]): list of values of the attribute for each components
-        attributeName (str): name of the attribute
-        componentNames (tuple[str,...]): name of the components for vectorial
-            attributes
-        onPoints (bool): True if attributes are on points, False if they are
-            on cells.
+        object (vtkDataObject): object (vtkMultiBlockDataSet, vtkDataSet) where to create the attribute.
+        values ( list[float]): list of values of the attribute for each components.
+        attributeName (str): name of the attribute.
+        componentNames (tuple[str,...], optional): name of the components for vectorial attributes. If one component, give an empty tuple.
+            Defaults to an empty tuple.
+        onPoints (bool): True if attributes are on points, False if they are on cells.
+            Defaults to False.
+        vtkDataType (Union(any, int), optional): vtk data type of the attribute to create.
+            Defaults to None, the type is given by the type of the array value.
+            Waring with int8, uint8 and int64 type of value, several vtk array type use it by default:
+            - int8 -> VTK_SIGNED_CHAR
+            - uint8 -> VTK_UNSIGNED_CHAR
+            - int64 -> VTK_LONG_LONG
 
     Returns:
-        bool: True if the attribute was correctly created
+        bool: True if the attribute was correctly created False if the attribute was already present.
     """
     if isinstance( object, ( vtkMultiBlockDataSet, vtkCompositeDataSet ) ):
-        return createConstantAttributeMultiBlock( object, values, attributeName, componentNames, onPoints )
+        return createConstantAttributeMultiBlock( object, values, attributeName, componentNames, onPoints, vtkDataType )
+
     elif isinstance( object, vtkDataSet ):
         listAttributes: set[ str ] = getAttributeSet( object, onPoints )
         if attributeName not in listAttributes:
-            return createConstantAttributeDataSet( object, values, attributeName, componentNames, onPoints )
-        return True
+            return createConstantAttributeDataSet( object, values, attributeName, componentNames, onPoints,
+                                                   vtkDataType )
+        print( "The attribute was already present in the vtkDataSet." )
+        return False
     return False
 
 
 def createConstantAttributeMultiBlock(
     multiBlockDataSet: Union[ vtkMultiBlockDataSet, vtkCompositeDataSet ],
-    values: list[ float ],
+    values: list[ Any ],
     attributeName: str,
-    componentNames: tuple[ str, ...],
-    onPoints: bool,
+    componentNames: tuple[ str, ...] = (),  # noqa: C408
+    onPoints: bool = False,
+    vtkDataType: Union[ int, Any ] = None,
 ) -> bool:
     """Create an attribute with a constant value everywhere if absent.
 
     Args:
-        multiBlockDataSet (vtkMultiBlockDataSet | vtkCompositeDataSet): vtkMultiBlockDataSet
-            where to create the attribute
-        values (list[float]): list of values of the attribute for each components
-        attributeName (str): name of the attribute
-        componentNames (tuple[str,...]): name of the components for vectorial
-            attributes
-        onPoints (bool): True if attributes are on points, False if they are
-            on cells.
+        multiBlockDataSet (vtkMultiBlockDataSet | vtkCompositeDataSet): vtkMultiBlockDataSet where to create the attribute.
+        values (list[any]): list of values of the attribute for each components.
+        attributeName (str): name of the attribute.
+        componentNames (tuple[str,...], optional): name of the components for vectorial attributes. If one component, give an empty tuple.
+            Defaults to an empty tuple.
+        onPoints (bool): True if attributes are on points, False if they are on cells.
+            Defaults to False.
+        vtkDataType (Union(any, int), optional): vtk data type of the attribute to create.
+            Defaults to None, the type is given by the type of the given value.
+            Waring with int8, uint8 and int64 type of value, several vtk array type use it by default:
+            - int8 -> VTK_SIGNED_CHAR
+            - uint8 -> VTK_UNSIGNED_CHAR
+            - int64 -> VTK_LONG_LONG
 
     Returns:
-        bool: True if the attribute was correctly created
+        bool: True if the attribute was correctly created, False if the attribute was already present.
     """
     # initialize data object tree iterator
+    checkCreat: bool = False
+
     iter: vtkDataObjectTreeIterator = vtkDataObjectTreeIterator()
     iter.SetDataSet( multiBlockDataSet )
     iter.VisitOnlyLeavesOn()
@@ -196,96 +238,133 @@ def createConstantAttributeMultiBlock(
         dataSet: vtkDataSet = vtkDataSet.SafeDownCast( iter.GetCurrentDataObject() )
         listAttributes: set[ str ] = getAttributeSet( dataSet, onPoints )
         if attributeName not in listAttributes:
-            createConstantAttributeDataSet( dataSet, values, attributeName, componentNames, onPoints )
+            checkCreat = createConstantAttributeDataSet( dataSet, values, attributeName, componentNames, onPoints,
+                                                         vtkDataType )
+
         iter.GoToNextItem()
-    return True
+
+    if checkCreat:
+        return True
+    else:
+        print( "The attribute was already present in the vtkMultiBlockDataSet." )
+        return False
 
 
 def createConstantAttributeDataSet(
     dataSet: vtkDataSet,
-    values: list[ float ],
+    values: list[ Any ],
     attributeName: str,
-    componentNames: tuple[ str, ...],
-    onPoints: bool,
+    componentNames: tuple[ str, ...] = (),  # noqa: C408
+    onPoints: bool = False,
+    vtkDataType: Union[ int, Any ] = None,
 ) -> bool:
     """Create an attribute with a constant value everywhere.
 
     Args:
-        dataSet (vtkDataSet): vtkDataSet where to create the attribute
-        values ( list[float]): list of values of the attribute for each components
-        attributeName (str): name of the attribute
-        componentNames (tuple[str,...]): name of the components for vectorial
-            attributes
-        onPoints (bool): True if attributes are on points, False if they are
-            on cells.
+        dataSet (vtkDataSet): vtkDataSet where to create the attribute.
+        values ( list[any]): list of values of the attribute for each components.
+        attributeName (str): name of the attribute.
+        componentNames (tuple[str,...], optional): name of the components for vectorial attributes. If one component, give an empty tuple.
+            Defaults to an empty tuple.
+        onPoints (bool): True if attributes are on points, False if they are on cells.
+            Defaults to False.
+        vtkDataType (Union(any, int), optional): vtk data type of the attribute to create.
+            Defaults to None, the type is given by the type of the given value.
+            Waring with int8, uint8 and int64 type of value, several vtk array type use it by default:
+            - int8 -> VTK_SIGNED_CHAR
+            - uint8 -> VTK_UNSIGNED_CHAR
+            - int64 -> VTK_LONG_LONG
 
     Returns:
-        bool: True if the attribute was correctly created
+        bool: True if the attribute was correctly created.
     """
     nbElements: int = ( dataSet.GetNumberOfPoints() if onPoints else dataSet.GetNumberOfCells() )
+
     nbComponents: int = len( values )
-    array: npt.NDArray[ np.float64 ] = np.ones( ( nbElements, nbComponents ) )
-    for i, val in enumerate( values ):
-        array[ :, i ] *= val
-    createAttribute( dataSet, array, attributeName, componentNames, onPoints )
-    return True
+    array: npt.NDArray[ Any ]
+    if nbComponents > 1:
+        array = np.array( [ values for _ in range( nbElements ) ] )
+    else:
+        array = np.array( [ values[ 0 ] for _ in range( nbElements ) ] )
+
+    return createAttribute( dataSet, array, attributeName, componentNames, onPoints, vtkDataType )
 
 
 def createAttribute(
     dataSet: vtkDataSet,
-    array: npt.NDArray[ np.float64 ],
+    array: npt.NDArray[ Any ],
     attributeName: str,
-    componentNames: tuple[ str, ...],
-    onPoints: bool,
+    componentNames: tuple[ str, ...] = (),  # noqa: C408
+    onPoints: bool = False,
+    vtkDataType: Union[ int, Any ] = None,
 ) -> bool:
-    """Create an attribute from the given array.
+    """Create an attribute and its VTK array from the given array.
 
     Args:
-        dataSet (vtkDataSet): dataSet where to create the attribute
-        array (npt.NDArray[np.float64]): array that contains the values
-        attributeName (str): name of the attribute
-        componentNames (tuple[str,...]): name of the components for vectorial
-            attributes
-        onPoints (bool): True if attributes are on points, False if they are
-            on cells.
+        dataSet (vtkDataSet): dataSet where to create the attribute.
+        array (npt.NDArray[any]): array that contains the values.
+        attributeName (str): name of the attribute.
+        componentNames (tuple[str,...], optional): name of the components for vectorial attributes. If one component, give an empty tuple.
+            Defaults to an empty tuple.
+        onPoints (bool): True if attributes are on points, False if they are on cells.
+            Defaults to False.
+        vtkDataType (Union(any, int), optional): vtk data type of the attribute to create.
+            Defaults to None, the type is given by the type of the given value in the array.
+            Waring with int8, uint8 and int64 type of value, several vtk array type use it. By default:
+            - int8 -> VTK_SIGNED_CHAR
+            - uint8 -> VTK_UNSIGNED_CHAR
+            - int64 -> VTK_LONG_LONG
 
     Returns:
-        bool: True if the attribute was correctly created
+        bool: True if the attribute was correctly created.
     """
     assert isinstance( dataSet, vtkDataSet ), "Attribute can only be created in vtkDataSet object."
 
-    newAttr: vtkDataArray = vnp.numpy_to_vtk( array, deep=True, array_type=VTK_DOUBLE )
-    newAttr.SetName( attributeName )
+    createdAttribute: vtkDataArray = vnp.numpy_to_vtk( array, deep=True, array_type=vtkDataType )
+    createdAttribute.SetName( attributeName )
 
-    nbComponents: int = newAttr.GetNumberOfComponents()
+    nbComponents: int = createdAttribute.GetNumberOfComponents()
     if nbComponents > 1:
+        nbNames = len( componentNames )
+
+        if nbNames < nbComponents:
+            componentNames = tuple( [ "Component" + str( i ) for i in range( nbComponents ) ] )
+            print( "Not enough component name enter, component names are seted to : Component0, Component1 ..." )
+        elif nbNames > nbComponents:
+            print( "To many component names enter, the lastest will not be taken into account." )
+
         for i in range( nbComponents ):
-            newAttr.SetComponentName( i, componentNames[ i ] )
+            createdAttribute.SetComponentName( i, componentNames[ i ] )
 
     if onPoints:
-        dataSet.GetPointData().AddArray( newAttr )
+        dataSet.GetPointData().AddArray( createdAttribute )
     else:
-        dataSet.GetCellData().AddArray( newAttr )
+        dataSet.GetCellData().AddArray( createdAttribute )
+
     dataSet.Modified()
+
     return True
 
 
 def copyAttribute(
     objectFrom: vtkMultiBlockDataSet,
     objectTo: vtkMultiBlockDataSet,
-    attributNameFrom: str,
-    attributNameTo: str,
+    attributeNameFrom: str,
+    attributeNameTo: str,
+    onPoints: bool = False,
 ) -> bool:
-    """Copy a cell attribute from objectFrom to objectTo.
+    """Copy an attribute from objectFrom to objectTo.
 
     Args:
         objectFrom (vtkMultiBlockDataSet): object from which to copy the attribute.
         objectTo (vtkMultiBlockDataSet): object where to copy the attribute.
-        attributNameFrom (str): attribute name in objectFrom.
-        attributNameTo (str): attribute name in objectTo.
+        attributeNameFrom (str): attribute name in objectFrom.
+        attributeNameTo (str): attribute name in objectTo.
+        onPoints (bool, optional): True if attributes are on points, False if they are on cells.
+            Defaults to False.
 
     Returns:
-        bool: True if copy successfully ended, False otherwise
+        bool: True if copy successfully ended, False otherwise.
     """
     elementaryBlockIndexesTo: list[ int ] = getBlockElementIndexesFlatten( objectTo )
     elementaryBlockIndexesFrom: list[ int ] = getBlockElementIndexesFlatten( objectFrom )
@@ -295,44 +374,53 @@ def copyAttribute(
 
     for index in elementaryBlockIndexesTo:
         # get block from initial time step object
-        blockT0: vtkDataSet = vtkDataSet.SafeDownCast( getBlockFromFlatIndex( objectFrom, index ) )
-        assert blockT0 is not None, "Block at initial time step is null."
+        blockFrom: vtkDataSet = vtkDataSet.SafeDownCast( getBlockFromFlatIndex( objectFrom, index ) )
+        assert blockFrom is not None, "Block at initial time step is null."
 
         # get block from current time step object
-        block: vtkDataSet = vtkDataSet.SafeDownCast( getBlockFromFlatIndex( objectTo, index ) )
-        assert block is not None, "Block at current time step is null."
+        blockTo: vtkDataSet = vtkDataSet.SafeDownCast( getBlockFromFlatIndex( objectTo, index ) )
+        assert blockTo is not None, "Block at current time step is null."
+
         try:
-            copyAttributeDataSet( blockT0, block, attributNameFrom, attributNameTo )
+            copyAttributeDataSet( blockFrom, blockTo, attributeNameFrom, attributeNameTo, onPoints )
         except AssertionError:
             # skip attribute if not in block
             continue
+
     return True
 
 
 def copyAttributeDataSet(
     objectFrom: vtkDataSet,
     objectTo: vtkDataSet,
-    attributNameFrom: str,
-    attributNameTo: str,
+    attributeNameFrom: str,
+    attributeNameTo: str,
+    onPoints: bool = False,
 ) -> bool:
-    """Copy a cell attribute from objectFrom to objectTo.
+    """Copy an attribute from objectFrom to objectTo.
 
     Args:
         objectFrom (vtkDataSet): object from which to copy the attribute.
         objectTo (vtkDataSet): object where to copy the attribute.
-        attributNameFrom (str): attribute name in objectFrom.
-        attributNameTo (str): attribute name in objectTo.
+        attributeNameFrom (str): attribute name in objectFrom.
+        attributeNameTo (str): attribute name in objectTo.
+        onPoints (bool, optional): True if attributes are on points, False if they are on cells.
+            Defaults to False.
 
     Returns:
-        bool: True if copy successfully ended, False otherwise
+        bool: True if copy successfully ended, False otherwise.
     """
     # get attribut from initial time step block
-    npArray: npt.NDArray[ np.float64 ] = getArrayInObject( objectFrom, attributNameFrom, False )
+    npArray: npt.NDArray[ Any ] = getArrayInObject( objectFrom, attributeNameFrom, onPoints )
     assert npArray is not None
-    componentNames: tuple[ str, ...] = getComponentNames( objectFrom, attributNameFrom, False )
+
+    componentNames: tuple[ str, ...] = getComponentNames( objectFrom, attributeNameFrom, onPoints )
+    vtkDataType: int = getVtkArrayTypeInObject( objectFrom, attributeNameFrom, onPoints )
+
     # copy attribut to current time step block
-    createAttribute( objectTo, npArray, attributNameTo, componentNames, False )
+    createAttribute( objectTo, npArray, attributeNameTo, componentNames, onPoints, vtkDataType )
     objectTo.Modified()
+
     return True
 
 
@@ -345,9 +433,9 @@ def renameAttribute(
     """Rename an attribute.
 
     Args:
-        object (vtkMultiBlockDataSet): object where the attribute is
-        attributeName (str): name of the attribute
-        newAttributeName (str): new name of the attribute
+        object (vtkMultiBlockDataSet): object where the attribute is.
+        attributeName (str): name of the attribute.
+        newAttributeName (str): new name of the attribute.
         onPoints (bool): True if attributes are on points, False if they are on cells.
 
     Returns:
diff --git a/geos-mesh/src/geos/mesh/utils/multiblockModifiers.py b/geos-mesh/src/geos/mesh/utils/multiblockModifiers.py
index ebbf2100..5f00afb8 100644
--- a/geos-mesh/src/geos/mesh/utils/multiblockModifiers.py
+++ b/geos-mesh/src/geos/mesh/utils/multiblockModifiers.py
@@ -29,8 +29,7 @@ def mergeBlocks(
 
     """
     if keepPartialAttributes:
-        fillAllPartialAttributes( input, False )
-        fillAllPartialAttributes( input, True )
+        fillAllPartialAttributes( input )
 
     af = vtkAppendDataSets()
     af.MergePointsOn()
diff --git a/geos-mesh/tests/conftest.py b/geos-mesh/tests/conftest.py
index 56a1de08..3e26dced 100644
--- a/geos-mesh/tests/conftest.py
+++ b/geos-mesh/tests/conftest.py
@@ -5,7 +5,7 @@
 # ruff: noqa: E402 # disable Module level import not at top of file
 import os
 import pytest
-from typing import Union
+from typing import Union, Any
 import numpy as np
 import numpy.typing as npt
 
@@ -15,6 +15,7 @@
 
 @pytest.fixture
 def arrayExpected( request: pytest.FixtureRequest ) -> npt.NDArray[ np.float64 ]:
+    """Get an array from a file."""
     reference_data = "data/data.npz"
     reference_data_path = os.path.join( os.path.dirname( os.path.realpath( __file__ ) ), reference_data )
     data = np.load( reference_data_path )
@@ -24,6 +25,7 @@ def arrayExpected( request: pytest.FixtureRequest ) -> npt.NDArray[ np.float64 ]
 
 @pytest.fixture
 def arrayTest( request: pytest.FixtureRequest ) -> npt.NDArray[ np.float64 ]:
+    """Get a random array of float64."""
     np.random.seed( 42 )
     array: npt.NDArray[ np.float64 ] = np.random.rand(
         request.param,
@@ -33,22 +35,93 @@ def arrayTest( request: pytest.FixtureRequest ) -> npt.NDArray[ np.float64 ]:
 
 
 @pytest.fixture
-def dataSetTest() -> Union[ vtkMultiBlockDataSet, vtkPolyData, vtkDataSet ]:
+def getArrayWithSpeTypeValue() -> Any:
+    """Get a random array of input type with the function _getarray().
 
-    def _get_dataset( datasetType: str ):
+    Returns:
+        npt.NDArray[Any]: random array of input type.
+    """
+
+    def _getarray( nb_component: int, nb_elements: int, valueType: str ) -> Any:
+        """Get a random array of input type.
+
+        Args:
+            nb_component (int): nb of components.
+            nb_elements (int): nb of elements.
+            valueType (str): the type of the value.
+
+        Returns:
+            npt.NDArray[Any]: random array of input type.
+        """
+        if valueType == "int32":
+            if nb_component == 1:
+                return np.array( [ np.int32( 1000 * np.random.random() ) for _ in range( nb_elements ) ] )
+            else:
+                return np.array( [ [ np.int32( 1000 * np.random.random() ) for _ in range( nb_component ) ]
+                                   for _ in range( nb_elements ) ] )
+
+        elif valueType == "int64":
+            if nb_component == 1:
+                return np.array( [ np.int64( 1000 * np.random.random() ) for _ in range( nb_elements ) ] )
+            else:
+                return np.array( [ [ np.int64( 1000 * np.random.random() ) for _ in range( nb_component ) ]
+                                   for _ in range( nb_elements ) ] )
+
+        elif valueType == "float32":
+            if nb_component == 1:
+                return np.array( [ np.float32( 1000 * np.random.random() ) for _ in range( nb_elements ) ] )
+            else:
+                return np.array( [ [ np.float32( 1000 * np.random.random() ) for _ in range( nb_component ) ]
+                                   for _ in range( nb_elements ) ] )
+
+        else:
+            if nb_component == 1:
+                return np.array( [ np.float64( 1000 * np.random.random() ) for _ in range( nb_elements ) ] )
+            else:
+                return np.array( [ [ np.float64( 1000 * np.random.random() ) for _ in range( nb_component ) ]
+                                   for _ in range( nb_elements ) ] )
+
+    return _getarray
+
+
+@pytest.fixture
+def dataSetTest() -> Any:
+    """Get a vtkObject from a file with the function _get_dataset().
+
+    Returns:
+        (vtkMultiBlockDataSet, vtkPolyData, vtkDataSet): the vtk object.
+    """
+
+    def _get_dataset( datasetType: str ) -> Union[ vtkMultiBlockDataSet, vtkPolyData, vtkDataSet ]:
+        """Get a vtkObject from a file.
+
+        Args:
+            datasetType (str): the type of vtk object wanted.
+
+        Returns:
+            (vtkMultiBlockDataSet, vtkPolyData, vtkDataSet): the vtk object.
+        """
+        reader: Union[ vtkXMLMultiBlockDataReader, vtkXMLUnstructuredGridReader ]
         if datasetType == "multiblock":
-            reader = reader = vtkXMLMultiBlockDataReader()
+            reader = vtkXMLMultiBlockDataReader()
             vtkFilename = "data/displacedFault.vtm"
+        elif datasetType == "emptymultiblock":
+            reader = vtkXMLMultiBlockDataReader()
+            vtkFilename = "data/displacedFaultempty.vtm"
         elif datasetType == "dataset":
-            reader: vtkXMLUnstructuredGridReader = vtkXMLUnstructuredGridReader()
+            reader = vtkXMLUnstructuredGridReader()
             vtkFilename = "data/domain_res5_id.vtu"
+        elif datasetType == "emptydataset":
+            reader = vtkXMLUnstructuredGridReader()
+            vtkFilename = "data/domain_res5_id_empty.vtu"
         elif datasetType == "polydata":
-            reader: vtkXMLUnstructuredGridReader = vtkXMLUnstructuredGridReader()
+            reader = vtkXMLUnstructuredGridReader()
             vtkFilename = "data/surface.vtu"
+
         datapath: str = os.path.join( os.path.dirname( os.path.realpath( __file__ ) ), vtkFilename )
         reader.SetFileName( datapath )
         reader.Update()
 
         return reader.GetOutput()
 
-    return _get_dataset
\ No newline at end of file
+    return _get_dataset
diff --git a/geos-mesh/tests/data/displacedFaultempty.vtm b/geos-mesh/tests/data/displacedFaultempty.vtm
new file mode 100644
index 00000000..20ff57fb
--- /dev/null
+++ b/geos-mesh/tests/data/displacedFaultempty.vtm
@@ -0,0 +1,7 @@
+<?xml version="1.0"?>
+<VTKFile type="vtkMultiBlockDataSet" version="1.0">
+    <vtkMultiBlockDataSet>
+        <DataSet name="main" file="domain_res5_id_empty.vtu"/>
+        <DataSet name="fracture" file="fracture_res5_id_empty.vtu"/>
+    </vtkMultiBlockDataSet>
+</VTKFile>
diff --git a/geos-mesh/tests/data/domain_res5_id_empty.vtu b/geos-mesh/tests/data/domain_res5_id_empty.vtu
new file mode 100644
index 00000000..94b5c796
--- /dev/null
+++ b/geos-mesh/tests/data/domain_res5_id_empty.vtu
@@ -0,0 +1,39 @@
+<?xml version="1.0"?>
+<VTKFile type="UnstructuredGrid" version="0.1" byte_order="LittleEndian" header_type="UInt32" compressor="vtkZLibDataCompressor">
+  <UnstructuredGrid>
+    <Piece NumberOfPoints="4092"                 NumberOfCells="1740"                >
+      <PointData GlobalIds="GLOBAL_IDS_POINTS">
+        <DataArray type="Int64" IdType="1" Name="GLOBAL_IDS_POINTS" format="appended" RangeMin="0"                    RangeMax="4091"                 offset="0"                   >
+        </DataArray>
+      </PointData>
+      <CellData GlobalIds="GLOBAL_IDS_CELLS">
+        <DataArray type="Int32" Name="CELL_MARKERS" format="appended" RangeMin="1"                    RangeMax="3"                    offset="8820"                >
+        </DataArray>
+        <DataArray type="Int64" IdType="1" Name="GLOBAL_IDS_CELLS" format="appended" RangeMin="0"                    RangeMax="1739"                 offset="9464"                >
+        </DataArray>
+      </CellData>
+      <Points>
+        <DataArray type="Float32" Name="Points" NumberOfComponents="3" format="appended" RangeMin="0"                    RangeMax="3221.0246817"         offset="13312"               >
+          <InformationKey name="L2_NORM_RANGE" location="vtkDataArray" length="2">
+            <Value index="0">
+              0
+            </Value>
+            <Value index="1">
+              3221.0246817
+            </Value>
+          </InformationKey>
+        </DataArray>
+      </Points>
+      <Cells>
+        <DataArray type="Int64" Name="connectivity" format="appended" RangeMin=""                     RangeMax=""                     offset="40324"               />
+        <DataArray type="Int64" Name="offsets" format="appended" RangeMin=""                     RangeMax=""                     offset="81108"               />
+        <DataArray type="UInt8" Name="types" format="appended" RangeMin=""                     RangeMax=""                     offset="84640"               />
+        <DataArray type="Int64" IdType="1" Name="faces" format="appended" RangeMin=""                     RangeMax=""                     offset="84696"               />
+        <DataArray type="Int64" IdType="1" Name="faceoffsets" format="appended" RangeMin=""                     RangeMax=""                     offset="193288"              />
+      </Cells>
+    </Piece>
+  </UnstructuredGrid>
+  <AppendedData encoding="base64">
+   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AQAAAACAAAAwGwAABAEAAA==eJztlksOxDAIQ9PO/e88mq3lZ6JoVKURC5SGfADjUO4xxv1yuUA+BwjF9k95IkenYNX52CsfapNivWVddTTSWrXf+aO66p50ZsZvnV9wD9msbLk67HCajTt9u7WK34ShyythnHhA8VB8Dh/CM+FGOFexJRySn2rbYULcUl4QpitvKPHW2a14f4p0P9T/36ew6nzslY+Z+ubqp+popLVqv/NHddU96cyM3zrvfoh5QRgnHlA8FF/3Q4zpyhtKvHV2K96fIt0P9f/3Kaw6H3vlY6a+ufqpOhpprdrv/FFddU86M+O3zrsfYl4QxokHFA/F1/0QY7ryhhJvnd2K9z/5Ao84Duw=AQAAAACAAACQUQAAMQAAAA==eJztwzENAAAIA7B3SjCBx2lGCG3SbCeqqqqqqqqqqqqqqqqqqqqqqqqqqo8eaqCtmg==AQAAAACAAAAwGwAAIwAAAA==eJztwwENAAAIA6BmJjC67/QgwkZuJ6qqqqqqqvp0AWlKhrc=AQAAAACAAAAwGwAAPQAAAA==eJzt1rEJADAIRUGH/dl/hbSp0oiFcAci2Nm9VFUG5wxPnp3Pfet/AMC87b2ghwCAru29oIcAgK4L9At6fQ==AQAAAACAAABgNgAAawoAAA==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eJztyDENACAMADA8ERLOqUEt87QwG7Rn8z47Tpsx2or03nvvvffee++9995777333nvvvffee++9995777333nvvvffee++9995777333nvvvffee++9995777333nvvvffee++9995777333nvvP/oCvK2ftnic7cgxDQAgDAAwPBESzqmZWuaJDBmkPZvZZoxnRZ22I7333nvvvffee++9995777333nvvvf/gLz7+gf8=AgAAAACAAADQPwAAnzAAAG4eAAA=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BQAAAACAAADAOgAATRoAAAgbAAAyGwAAIxoAALEMAAA=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AQAAAACAAADMBgAAFgAAAA==eJzT0hoFo2AUjIJRMAqGNgAA9C4diA==EQAAAACAAADAcAAAVRMAAFMRAADuEQAAZhMAANwTAAD/EgAAqBIAADwRAACTEgAAbRQAAHwTAACZEwAAxBEAACYRAADmEgAAehQAALgQAAA=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AQAAAACAAABgNgAAMAwAAA==eJwt22m8SHX+wPFEJFxlzV66KSVFlyJSSZRE3UTZyZItS4wsdyy52WUp2bJl7HshGqT+rm1cZJCUZSwxuGO9I4b/g+/7PHk/+Zzf6/d9eM75nTJ3xFWLnTiOy7mfN1k6S/gi23M0F3MHL/LeO8PKbMp+nMU0nmLurGFFNmYKv+aPPMO82cJKbMK+nMyNPMlcd4WPsQF783Ou5zHekT0ow1rsxHFcwK08zdsslyNsyT6cxNXcxSssenf4AltwFBdxDzNZKGdYiU04iPO4heeY757wWTbiAM7hNv7BhFxhEt/jQM7gTv6b9+UOn2Ez9udsbuVp5skTPs232Idf8Sf+izkTwif5Bj/iKH7HI7zFxLxhTbbmMM7jPl7mA/eGNdiZI7mCu5nBhPvCJ/g6O3Ecl3M/L7BAvrAqW/KvnMsdPMvC+cPKbMp+nMU0nmf+AmFFNmYKv+Z2nmHegmElNmFPzuQWnmSuQmEFNmJvTuNmHmOOwuETfJ09+CXX8DdmvT8syzrsytFczL28yMJFwupsysGcxU08xbuKhhX5JlM4ndt5hnmLheVZj505kgu5jRlMKB4mMZk9OI7Lmc4LzFMiLMe67MjhnM80nuJdJcNE1mRrDuI8buFl5isV1mBzfsKF3MYMFnzA3Ezmx5zBDTzBex4My7I+e3Eq1/Eos5cOH2Zdduck/sTDvPOh8FHWZhdO4Eoe5C2WSAxr8gMO41Lu42UWeTisweb8hAu5mxksWCZ8jsnswXFcznReYJ5HwnKsy44czvlM4yne9WiYyJpszUGcyS08yXxlwwpsywGcwwP8H4s/Zm624qdcwkPM8bieHTiGm5nB4uXCBuzNadzMDD70hJ6fcCEPMFt587I5x3Mjz/GBJ8N67MmZ3Me7nworsQmHcSnPsESF8DWmcBEP8hYfrxg2ZgoX8QhzPx2+wX5cxsPMmRRWZx+u42kWqBTWYVdO5VZeYIHKevbiVK7jaRZ4Rs+uXMD9vOfZ8Bl24gzu5HWWqRIm81N+w2NMqBomsRU/5XoeY8Jz4XNsxU+5nhksXi18lb25kAeYrXr4LNtyPDfyHIs8H9ZgW47nRp5krhrWZ1tO5kZe5gMvhPU4iEu5j1leDMuzHjtzJNfyJHO9FFZgI47kWp5jkZrhK+zLmfwH/+QjL9sPe3Im/8EsHizLszXH8nseZ6FXwprsxunczkwm1g4bM4WLuIe3mFgnfJNDuZJHmPvVsCLbcQp38QYffS1syMFczbMsXDd8mW04mKt5kSVfD+uyD+dyL7PXC59mS07kD7zJsm+E73AE1/ASS9UPX+fH/Bt/5h0NgiS24hhuZgYfetNtHMCF3M1sb4WPsRFHci1PMl9y+Cw7czw38iSLvB2+wp5cyuMs1DB8jd04ndt5i4nv6JnCRTzC3I3CKmzHUfyOp5i/cfgCu3AK03iFRd/VswVTOJ3f8zjvfi8sz3rsyclcy9+ZrUn4GF/lh/yc3/BnXuf9TcPn+T6HcAHTeYEFmoVV2ZKpXMwdvMjCzcPqbMPBnMU0nmf+FmFFNmYKp/NHnuHdLcPyrMeenMwV3M0M5mgVJrEDB3IGN3A/b7J067AO23MiV/EX3mbJNmF1tuFwLuMuXmHR98MX2IJDuYh7+B8WahtWY2sO4zzu42UWaRfWYFt+woXczQwWbB8+x/c4kH/jTv6b93UIn+E77M+J/IGnmf2DsBzrsiM/4zIe5g0+2jGszY84gd/xIDNZyAveSnybPTmZa/k7r7F45/AlduCnXMKfeYmluoTP830O4QKm8yqLdQ2rsiVTuZh7eZGFPwyrsw37cT538TzzdwursAVT+DW38wzzdg8r8W325Uxu5Enm6hFWYAP25uf8hod4naV6hrX4PkdwAbfyAvN8FFblu0zlXO7lRRbuFVZmQ37ECVzJPcxkod5hNTZhX07mWh7gNRb8S5jEZPbgOC5nOi8wT5+wHOuyI4dzGXfxBot+HNZmO47iSu5hJkv0NTebcBDncQvPMV+/sAIbcQDncDP/YEL/8Akm82PO4E6e4D0DwqdYn704let4lNlTwodZl935GVfzMG/wwb+GtdmOo7iSB5nJEgPDmmzCvpzMtTzAayw4KExiMntwHJcznReYZ3BYjnXZkcM5n7t4nkWHhFXYhUO5iEd41ydhImvyA47ltzzOvEP17MYvuJ2ZTEwNGzOFX3M7M/n4p3qO4koeYe5h5mU7TmEar/DR4WFD9uN8Hua9I8LqbMPPuJoX+fDI8C2mchWPMvuo8Gm2ZCpX8TQLjA7f4RCu4QneNyasxYHczAwWHxs2YG/O4W5eY/HP9BzAOdzMDBb3obcBe3MFf2e+8WEN9uQ87mOWCWF5NuFYfs8zLDQxrMYPOJY/8gwLfR7W5Accyx+ZycQvwjeZwpU8wtyTwhfYhVOYxit88MuwNrtwCtN4nvknW59dOItpvMFHp4QNOZyreZg5p4aV2ZAfcQI38TzzTwursAUncBOv8MHp4RsczPn8J+/8KnySDdmP8/lP5pxh/+zISfyJZ1lyZliXfTiXe3mb5WaFLZnKVfyF2Wfr+S5Hcx1Ps8CcsCq7cjb3M+vX4VNsxhHcwEssNTd8nZ04ght4nWX+FiZzIJfwEBPmhc+xA6dxG7PNDyuwOcdzI//kIwvCtzmIS/kr714YVuMH/ILbmcnHF4WNOZQreZC5F4cV2YITuInnWXRJ+AI/4hSm8TwfXBq+wX5czbMsuSx8i304l3uZ3YGacnyLqVzF0yywInyRXTmRP/ACi60M67AXZzOdN1l6lZ7tmcq5/Ilnee83YWU2ZD/O4iaeYu5vw4p8k3/hdH7PX5lldfgAX2FnjuQKHuA1Fl8TvsQOHMNv+DOvs9TasBY7cQQXMJ1XWey7sCpbMpVzuYMXee+6sDIbsh9n8TseZCbzrg+rsRuHcR638Hdm+z58jA34IadxPY8xx9/DMqzFThzHNdzPmyy9IazD9hzNVfyF/2XJjeHL7MjPuIyHeYMPbgprswtHcSUPMpMlfghrsjWHcSn38TKLbA5rsDk/4TRuYwYTfgyTmMwe/JJreIJZfwqfYn3251T+wKO8zZL/F1ZnU/bjLG7iKd5i4pbwNXbjWH7LX/knH0kLX2FnjuQKHuD/+NDW8CV24Bh+w0O8zlLbwlrsxCFczv28ymLbwxfZnqlczL28yMI7wupsysGczzSeZ/6dYRU2Zgqn83seZ5Z/hI+wHjtzPFdwN6+x4K7wJbbiGC7hIV5nqfTweTZjf07lOv7C2yy5O3yZbTiYs7iJR3iLJfaE1diEfTmZa3mA11hwb5jEZPbgOK7hfmb9OSzN+uzKiVzHX3ibD+8zN9twOJdxF6+w6D/DKmzBoVzE7fwPCznQW4lNOIjzuI/nmO9A+CwbcQDncDP/YMLB8Akm82N+yQ08way/hGVZn105ket4lLf58KGwLttwMGdxE4/wFkv8GlZjE/blZK7lAV5jwcNhEpPZg+O4nPt5laV/C19kL47mKp5mnt/DcqzL7pzEv/MsCx/Rsw+/4l7eZrmjYUumcjH38jafPqbnRK7jaRY4bl525Wym8yaf+lfYjEO4nCd4/4mwFjvxS27gdT5xMnyPY7iefzDhVPgcO3AM1zODxU+HzTmSG3mORf4I63EYtzOTiWfCxkzhIh7kLSae1XMoF3E7M5n4bz1T+B1Psei5sDb7cRkPM+f5sDLbcBJ/4kWWvBC+zO6cxB28yJIZYV125yTu4G2W+0/4LlO5jqdZ4GJYh704m+m8ybKXwvrsxdlM51UWu2x99uICpjPrlfApNuM4buAJ3nc1fJ7N2J9TuZVXWexa+CLbcyq38ibLZobvcASX8zfe89/wGTbjEC7nb7zvuv2zB2dwJy+xzJ9hMgdyCQ8xx40wiR04hut5jAl+fEliK37Ozcxg8f+FL7E3F/J35roVPsu2HM8t/JOP3A7fZk+O5xZmuSN+uCnPJhzGb3mchbKENdmNX3MPc98ZVmE7TmEa78waPsmmHM7V/BfvzRa+zO78int5m0/fFbbkaK7jURbIHlZle07lVl5l6RxhHfbnbKbzKsveHb7DIdzASyyTM3yPA7mEh5hwT5jE9ziG65nB4rnCV9mb07iN1/hQ7rABB3AhDzBbnvAxNuCHHMMl3MlLvD8h/H98O5vm
+  </AppendedData>
+</VTKFile>
diff --git a/geos-mesh/tests/data/fracture_res5_id_empty.vtu b/geos-mesh/tests/data/fracture_res5_id_empty.vtu
new file mode 100644
index 00000000..a69adb95
--- /dev/null
+++ b/geos-mesh/tests/data/fracture_res5_id_empty.vtu
@@ -0,0 +1,41 @@
+<?xml version="1.0"?>
+<VTKFile type="UnstructuredGrid" version="0.1" byte_order="LittleEndian" header_type="UInt32" compressor="vtkZLibDataCompressor">
+  <UnstructuredGrid>
+    <Piece NumberOfPoints="212" NumberOfCells="156">
+      <PointData GlobalIds="GLOBAL_IDS_POINTS">
+        <DataArray type="Int64" IdType="1" Name="GLOBAL_IDS_POINTS" format="binary" RangeMin="0" RangeMax="211">
+          AQAAAACAAACgBgAAbgEAAA==eJwtxdciEAAAAEBRUmlpK9q0aEpbey/tTUMb0d57T6VBO+2NSGjvoflDHrp7uYCA/6o40EGu6moOdnWHuIZrupZDXdt1XNf1XN9hbuCGbuTGbuKmbuZwN3cLRzjSLd3Krd3Gbd3O7R3laHdwR3dyZ3dxjGPd1d3c3T3c070c596Odx/3dT/39wAP9CAneLCHeKiHebhHeKRHebTHeKzHebwneKInebITPcVTPc3TPcMzPcuzPcdzPc/zvcBJTvZCL/JiL3GKl3qZl3uFV3qVVzvVaU73Gmc402u9zuu9wRu9yZu9xVu9zdu9wzu9y7u9x3u9z/t9wAd9yId9xEd9zMd9wid9ylk+7TPO9lmf83lfcI5zfdGXfNlXfNXXfN03nOebvuXbvuO7vuf7fuCHfuTHfuKnzneBC/3MRS72c5f4hUtd5nK/9Cu/9hu/9Tu/9wd/9Cd/9hd/9Td/9w9X+Kd/+bf/+K//uRLqf1df
+        </DataArray>
+      </PointData>
+      <CellData GlobalIds="GLOBAL_IDS_CELLS">
+        <DataArray type="Int64" IdType="1" Name="GLOBAL_IDS_CELLS" format="binary" RangeMin="0" RangeMax="155">
+          AQAAAACAAADgBAAAEgEAAA==eJwtxddCCAAAAMAiozSkoaGh0NAeqGhrSNEg7SFkJKFhlIhoaCBUP9tDdy8XEHAo0Ed81EE+5uM+4ZMOdohPOdRhDneETzvSZxzlaMc41mcd53gnONHnnORkpzjV553mdF/wRV9yhjOd5Wxfdo5zned8F7jQRS52iUt9xVd9zWUud4Wv+4YrXeVq17jWda73TTe40U1u9i23+LZb3eY7vut2d7jTXb7n++72A/e4133u94AHPeRhj3jUDz3mR37sJx73Uz/zc7/whF960q885dd+47ee9oxnPed3fu8P/uh5L/iTF/3ZX7zkr/7mZX/3D6941Wte909veNNb3vYv//Yf7/iv//m/d73nfR8ARZMvOw==
+        </DataArray>
+      </CellData>
+      <Points>
+        <DataArray type="Float32" Name="Points" NumberOfComponents="3" format="binary" RangeMin="0" RangeMax="2304.886114323222">
+          AQAAAACAAADwCQAAXQIAAA==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
+          <InformationKey name="L2_NORM_RANGE" location="vtkDataArray" length="2">
+            <Value index="0">
+              0
+            </Value>
+            <Value index="1">
+              2304.8861143
+            </Value>
+          </InformationKey>
+        </DataArray>
+      </Points>
+      <Cells>
+        <DataArray type="Int64" Name="connectivity" format="binary" RangeMin="0" RangeMax="211">
+          AQAAAACAAACAEwAAWQMAAA==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
+        </DataArray>
+        <DataArray type="Int64" Name="offsets" format="binary" RangeMin="4" RangeMax="624">
+          AQAAAACAAADgBAAADgEAAA==eJwtxRFwAgAAAMC2C4IgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIgCAaDwSAIgiAIgiAIBkEQDPqXDwbeQg474qhjjjvhpFNOO+Osc8674KJLLrviqmuuu+GmW26746577nvgoUcee+KpZ5574aVXXnvjrb/87R//eue9Dz765LMvvvrmu//88NMvBz7eBR1y2BFHHXPcCSedctoZZ51z3gUXXXLZFVddc90NN91y2x133XPfAw898tgTTz3z3AsvvfLaG2/95W//+Nc7733w0SefffHVN9/954effjnw+S7okMOOOOqY40446ZTTzjjrnPMuuOiSy6646prrbrjpltvu+B9fwUXT
+        </DataArray>
+        <DataArray type="UInt8" Name="types" format="binary" RangeMin="7" RangeMax="7">
+          AQAAAACAAACcAAAADAAAAA==eJxjZx+8AABPhQRF
+        </DataArray>
+      </Cells>
+    </Piece>
+  </UnstructuredGrid>
+</VTKFile>
diff --git a/geos-mesh/tests/test_arrayHelpers.py b/geos-mesh/tests/test_arrayHelpers.py
index 0a73ee99..eeebd177 100644
--- a/geos-mesh/tests/test_arrayHelpers.py
+++ b/geos-mesh/tests/test_arrayHelpers.py
@@ -100,6 +100,37 @@ def test_getArrayInObject( request: pytest.FixtureRequest, arrayExpected: npt.ND
     assert ( obtained == expected ).all()
 
 
+@pytest.mark.parametrize( "attributeName, vtkDataType, onPoints", [
+    ( "CellAttribute", 11, False ),
+    ( "PointAttribute", 11, True ),
+    ( "collocated_nodes", 12, True ),
+    ( "collocated_nodes", -1, False ),
+    ( "newAttribute", -1, False ),
+] )
+def test_getVtkArrayTypeInMultiBlock( dataSetTest: vtkMultiBlockDataSet, attributeName: str, vtkDataType: int,
+                                      onPoints: bool ) -> None:
+    """Test getting the type of the vtk array of an attribute from multiBlockDataSet."""
+    multiBlockDataSet: vtkMultiBlockDataSet = dataSetTest( "multiblock" )
+
+    vtkDataTypeTest: int = arrayHelpers.getVtkArrayTypeInMultiBlock( multiBlockDataSet, attributeName, onPoints )
+
+    assert ( vtkDataType == vtkDataTypeTest )
+
+
+@pytest.mark.parametrize( "attributeName, onPoints", [
+    ( "CellAttribute", False ),
+    ( "PointAttribute", True ),
+] )
+def test_getVtkArrayTypeInObject( dataSetTest: vtkDataSet, attributeName: str, onPoints: bool ) -> None:
+    """Test getting the type of the vtk array of an attribute from dataset."""
+    vtkDataSetTest: vtkDataSet = dataSetTest( "dataset" )
+
+    obtained: int = arrayHelpers.getVtkArrayTypeInObject( vtkDataSetTest, attributeName, onPoints )
+    expected: int = 11
+
+    assert ( obtained == expected )
+
+
 @pytest.mark.parametrize( "arrayExpected, onpoints", [
     ( "PORO", False ),
     ( "PointAttribute", True ),
diff --git a/geos-mesh/tests/test_arrayModifiers.py b/geos-mesh/tests/test_arrayModifiers.py
index 5f90bb13..3aff05c4 100644
--- a/geos-mesh/tests/test_arrayModifiers.py
+++ b/geos-mesh/tests/test_arrayModifiers.py
@@ -1,82 +1,151 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright 2023-2024 TotalEnergies.
-# SPDX-FileContributor: Paloma Martinez
+# SPDX-FileContributor: Paloma Martinez, Romain Baville
 # SPDX-License-Identifier: Apache 2.0
 # ruff: noqa: E402 # disable Module level import not at top of file
 # mypy: disable-error-code="operator"
 import pytest
-from typing import Union, Tuple, cast
+from typing import Union, Any, cast
 
 import numpy as np
 import numpy.typing as npt
 
 import vtkmodules.util.numpy_support as vnp
-from vtkmodules.vtkCommonCore import vtkDataArray, vtkDoubleArray
-from vtkmodules.vtkCommonDataModel import ( vtkDataSet, vtkMultiBlockDataSet, vtkDataObjectTreeIterator, vtkPointData,
-                                            vtkCellData )
+from vtkmodules.vtkCommonCore import vtkDataArray
+from vtkmodules.vtkCommonDataModel import ( vtkDataSet, vtkMultiBlockDataSet, vtkPointData, vtkCellData )
+
+from geos.mesh.utils.arrayHelpers import getAttributesWithNumberOfComponents
 
 from vtk import (  # type: ignore[import-untyped]
-    VTK_CHAR, VTK_DOUBLE, VTK_FLOAT, VTK_INT, VTK_UNSIGNED_INT,
+    VTK_CHAR, VTK_DOUBLE, VTK_FLOAT, VTK_INT, VTK_UNSIGNED_INT, VTK_LONG_LONG, VTK_ID_TYPE,
 )
 
+# Informations :
+#     vtk array type       int   numpy type
+# VTK_CHAR               = 2  = np.int8
+# VTK_SIGNED_CHAR        = 15 = np.int8
+# VTK_SHORT              = 4  = np.int16
+# VTK_INT                = 6  = np.int32
+# VTK_BIT                = 1  = np.uint8
+# VTK_UNSIGNED_CHAR      = 3  = np.uint8
+# VTK_UNSIGNED_SHORT     = 5  = np.uint16
+# VTK_UNSIGNED_INT       = 7  = np.uint32
+# VTK_UNSIGNED_LONG_LONG = 17 = np.uint64
+# VTK_LONG               = 8  = LONG_TYPE_CODE ( int32 | int64 )
+# VTK_UNSIGNED_LONG      = 9  = ULONG_TYPE_CODE ( uint32 | uint64 )
+# VTK_FLOAT              = 10 = np.float32
+# VTK_DOUBLE             = 11 = np.float64
+# VTK_ID_TYPE            = 12 = ID_TYPE_CODE ( int32 | int64 )
+
+#     vtk array type       int  IdType  numpy type
+# VTK_LONG_LONG          = 16 = 2 = np.int64
+
 from geos.mesh.utils import arrayModifiers
 
 
-@pytest.mark.parametrize( "attributeName, onpoints", [ ( "CellAttribute", False ), ( "PointAttribute", True ) ] )
+@pytest.mark.parametrize(
+    "idBlockToFill, attributeName, nbComponentsRef, componentNamesRef, onPoints, value, valueRef, vtkDataTypeRef, valueTypeRef",
+    [
+        ( 1, "CellAttribute", 3, ( "AX1", "AX2", "AX3" ), False, np.nan, np.nan, VTK_DOUBLE, "float64" ),
+        ( 1, "CellAttribute", 3,
+          ( "AX1", "AX2", "AX3" ), False, np.float64( 4 ), np.float64( 4 ), VTK_DOUBLE, "float64" ),
+        ( 1, "CellAttribute", 3,
+          ( "AX1", "AX2", "AX3" ), False, np.int32( 4 ), np.float64( 4 ), VTK_DOUBLE, "float64" ),
+        ( 1, "PointAttribute", 3, ( "AX1", "AX2", "AX3" ), True, np.nan, np.nan, VTK_DOUBLE, "float64" ),
+        ( 1, "PointAttribute", 3,
+          ( "AX1", "AX2", "AX3" ), True, np.float64( 4 ), np.float64( 4 ), VTK_DOUBLE, "float64" ),
+        ( 1, "PointAttribute", 3,
+          ( "AX1", "AX2", "AX3" ), True, np.int32( 4 ), np.float64( 4 ), VTK_DOUBLE, "float64" ),
+        ( 1, "PORO", 1, (), False, np.nan, np.nan, VTK_FLOAT, "float32" ),
+        ( 1, "PORO", 1, (), False, np.float32( 4 ), np.float32( 4 ), VTK_FLOAT, "float32" ),
+        ( 1, "PORO", 1, (), False, np.int32( 4 ), np.float32( 4 ), VTK_FLOAT, "float32" ),
+        ( 1, "FAULT", 1, (), False, np.nan, np.int32( -1 ), VTK_INT, "int32" ),
+        ( 1, "FAULT", 1, (), False, np.int32( 4 ), np.int32( 4 ), VTK_INT, "int32" ),
+        ( 1, "FAULT", 1, (), False, np.float32( 4 ), np.int32( 4 ), VTK_INT, "int32" ),
+        ( 0, "collocated_nodes", 2, ( None, None ), True, np.nan, np.int64( -1 ), VTK_ID_TYPE, "int64" ),
+        ( 0, "collocated_nodes", 2, ( None, None ), True, np.int64( 4 ), np.int64( 4 ), VTK_ID_TYPE, "int64" ),
+        ( 0, "collocated_nodes", 2, ( None, None ), True, np.int32( 4 ), np.int64( 4 ), VTK_ID_TYPE, "int64" ),
+        ( 0, "collocated_nodes", 2, ( None, None ), True, np.float32( 4 ), np.int64( 4 ), VTK_ID_TYPE, "int64" ),
+    ] )
 def test_fillPartialAttributes(
     dataSetTest: vtkMultiBlockDataSet,
+    idBlockToFill: int,
     attributeName: str,
-    onpoints: bool,
+    nbComponentsRef: int,
+    componentNamesRef: tuple[ str, ...],
+    onPoints: bool,
+    value: Any,
+    valueRef: Any,
+    vtkDataTypeRef: int,
+    valueTypeRef: str,
 ) -> None:
-    """Test filling a partial attribute from a multiblock with nan values."""
-    vtkMultiBlockDataSetTest: vtkMultiBlockDataSet = dataSetTest( "multiblock" )
-    arrayModifiers.fillPartialAttributes( vtkMultiBlockDataSetTest, attributeName, nbComponents=3, onPoints=onpoints )
-
-    iter: vtkDataObjectTreeIterator = vtkDataObjectTreeIterator()
-    iter.SetDataSet( vtkMultiBlockDataSetTest )
-    iter.VisitOnlyLeavesOn()
-    iter.GoToFirstItem()
-    while iter.GetCurrentDataObject() is not None:
-        dataset: vtkDataSet = vtkDataSet.SafeDownCast( iter.GetCurrentDataObject() )
-        data: Union[ vtkPointData, vtkCellData ]
-        if onpoints:
-            data = dataset.GetPointData()
-        else:
-            data = dataset.GetCellData()
-        assert data.HasArray( attributeName ) == 1
+    """Test filling a partial attribute from a multiblock with values."""
+    MultiBlockDataSetTest: vtkMultiBlockDataSet = dataSetTest( "multiblock" )
+    arrayModifiers.fillPartialAttributes( MultiBlockDataSetTest, attributeName, onPoints, value )
+
+    blockTest: vtkDataSet = cast( vtkDataSet, MultiBlockDataSetTest.GetBlock( idBlockToFill ) )
+    dataTest: Union[ vtkPointData, vtkCellData ]
+    nbElements: int
+    if onPoints:
+        nbElements = blockTest.GetNumberOfPoints()
+        dataTest = blockTest.GetPointData()
+    else:
+        nbElements = blockTest.GetNumberOfCells()
+        dataTest = blockTest.GetCellData()
+
+    attributeFillTest: vtkDataArray = dataTest.GetArray( attributeName )
+    nbComponentsTest: int = attributeFillTest.GetNumberOfComponents()
+    assert nbComponentsRef == nbComponentsTest
+
+    npArrayFillRef: npt.NDArray[ Any ]
+    if nbComponentsRef > 1:
+        componentNamesTest: tuple[ str, ...] = tuple(
+            attributeFillTest.GetComponentName( i ) for i in range( nbComponentsRef ) )
+        assert componentNamesRef == componentNamesTest
+
+        npArrayFillRef = np.array( [ [ valueRef for _ in range( nbComponentsRef ) ] for _ in range( nbElements ) ] )
+    else:
+        npArrayFillRef = np.array( [ valueRef for _ in range( nbElements ) ] )
+
+    npArrayFillTest: npt.NDArray[ Any ] = vnp.vtk_to_numpy( attributeFillTest )
+    assert valueTypeRef == npArrayFillTest.dtype
 
-        iter.GoToNextItem()
+    if np.isnan( valueRef ):
+        assert np.isnan( npArrayFillRef ).all()
+    else:
+        assert ( npArrayFillRef == npArrayFillTest ).all()
+
+    vtkDataTypeTest: int = attributeFillTest.GetDataType()
+    assert vtkDataTypeRef == vtkDataTypeTest
 
 
-@pytest.mark.parametrize( "onpoints, expectedArrays", [
-    ( True, ( "PointAttribute", "collocated_nodes" ) ),
-    ( False, ( "CELL_MARKERS", "CellAttribute", "FAULT", "PERM", "PORO" ) ),
+@pytest.mark.parametrize( "value", [
+    ( np.nan ),
+    ( np.int32( 42 ) ),
+    ( np.int64( 42 ) ),
+    ( np.float32( 42 ) ),
+    ( np.float64( 42 ) ),
 ] )
-def test_fillAllPartialAttributes(
+def test_FillAllPartialAttributes(
     dataSetTest: vtkMultiBlockDataSet,
-    onpoints: bool,
-    expectedArrays: tuple[ str, ...],
+    value: Any,
 ) -> None:
-    """Test filling all partial attributes from a multiblock with nan values."""
-    vtkMultiBlockDataSetTest: vtkMultiBlockDataSet = dataSetTest( "multiblock" )
-    arrayModifiers.fillAllPartialAttributes( vtkMultiBlockDataSetTest, onpoints )
-
-    iter: vtkDataObjectTreeIterator = vtkDataObjectTreeIterator()
-    iter.SetDataSet( vtkMultiBlockDataSetTest )
-    iter.VisitOnlyLeavesOn()
-    iter.GoToFirstItem()
-    while iter.GetCurrentDataObject() is not None:
-        dataset: vtkDataSet = vtkDataSet.SafeDownCast( iter.GetCurrentDataObject() )
-        data: Union[ vtkPointData, vtkCellData ]
-        if onpoints:
-            data = dataset.GetPointData()
-        else:
-            data = dataset.GetCellData()
+    """Test to fill all the partial attributes of a vtkMultiBlockDataSet with a value."""
+    MultiBlockDataSetRef: vtkMultiBlockDataSet = dataSetTest( "multiblock" )
+    MultiBlockDataSetTest: vtkMultiBlockDataSet = dataSetTest( "multiblock" )
+    arrayModifiers.fillAllPartialAttributes( MultiBlockDataSetTest, value )
 
-        for attribute in expectedArrays:
-            assert data.HasArray( attribute ) == 1
+    nbBlock = MultiBlockDataSetRef.GetNumberOfBlocks()
+    for idBlock in range( nbBlock ):
+        datasetTest: vtkDataSet = cast( vtkDataSet, MultiBlockDataSetTest.GetBlock( idBlock ) )
+        for onPoints in [ True, False ]:
+            infoAttributes: dict[ str, int ] = getAttributesWithNumberOfComponents( MultiBlockDataSetRef, onPoints )
+            dataTest: Union[ vtkPointData, vtkCellData ]
+            dataTest = datasetTest.GetPointData() if onPoints else datasetTest.GetCellData()
 
-        iter.GoToNextItem()
+            for attributeName in infoAttributes:
+                attributeTest: int = dataTest.HasArray( attributeName )
+                assert attributeTest == 1
 
 
 @pytest.mark.parametrize( "attributeName, dataType, expectedDatatypeArray", [
@@ -101,139 +170,402 @@ def test_createEmptyAttribute(
     assert newAttr.IsA( str( expectedDatatypeArray ) )
 
 
-@pytest.mark.parametrize( "onpoints, elementSize", [
-    ( False, ( 1740, 156 ) ),
-    ( True, ( 4092, 212 ) ),
+@pytest.mark.parametrize( "attributeName, isNewOnBlock, onPoints", [
+    ( "newAttribute", ( True, True ), False ),
+    ( "newAttribute", ( True, True ), True ),
+    ( "PORO", ( True, True ), True ),
+    ( "PORO", ( False, True ), False ),
+    ( "PointAttribute", ( False, True ), True ),
+    ( "PointAttribute", ( True, True ), False ),
+    ( "collocated_nodes", ( True, False ), True ),
+    ( "collocated_nodes", ( True, True ), False ),
 ] )
 def test_createConstantAttributeMultiBlock(
     dataSetTest: vtkMultiBlockDataSet,
-    onpoints: bool,
-    elementSize: Tuple[ int, ...],
+    attributeName: str,
+    isNewOnBlock: tuple[ bool, ...],
+    onPoints: bool,
 ) -> None:
     """Test creation of constant attribute in multiblock dataset."""
-    vtkMultiBlockDataSetTest: vtkMultiBlockDataSet = dataSetTest( "multiblock" )
-    attributeName: str = "testAttributemultiblock"
-    values: tuple[ float, float, float ] = ( 12.4, 10, 40.0 )
-    componentNames: tuple[ str, str, str ] = ( "X", "Y", "Z" )
-    arrayModifiers.createConstantAttributeMultiBlock( vtkMultiBlockDataSetTest, values, attributeName, componentNames,
-                                                      onpoints )
-
-    iter: vtkDataObjectTreeIterator = vtkDataObjectTreeIterator()
-    iter.SetDataSet( vtkMultiBlockDataSetTest )
-    iter.VisitOnlyLeavesOn()
-    iter.GoToFirstItem()
-    while iter.GetCurrentDataObject() is not None:
-        dataset: vtkDataSet = vtkDataSet.SafeDownCast( iter.GetCurrentDataObject() )
-        data: Union[ vtkPointData, vtkCellData ]
-        if onpoints:
-            data = dataset.GetPointData()
+    MultiBlockDataSetRef: vtkMultiBlockDataSet = dataSetTest( "multiblock" )
+    MultiBlockDataSetTest: vtkMultiBlockDataSet = dataSetTest( "multiblock" )
+    values: list[ float ] = [ np.nan ]
+    arrayModifiers.createConstantAttributeMultiBlock( MultiBlockDataSetTest, values, attributeName, onPoints=onPoints )
+
+    nbBlock = MultiBlockDataSetRef.GetNumberOfBlocks()
+    for idBlock in range( nbBlock ):
+        datasetRef: vtkDataSet = cast( vtkDataSet, MultiBlockDataSetRef.GetBlock( idBlock ) )
+        datasetTest: vtkDataSet = cast( vtkDataSet, MultiBlockDataSetTest.GetBlock( idBlock ) )
+        dataRef: Union[ vtkPointData, vtkCellData ]
+        dataTest: Union[ vtkPointData, vtkCellData ]
+        if onPoints:
+            dataRef = datasetRef.GetPointData()
+            dataTest = datasetTest.GetPointData()
         else:
-            data = dataset.GetCellData()
-        createdAttribute: vtkDoubleArray = data.GetArray( attributeName )
-        cnames: Tuple[ str, ...] = tuple( createdAttribute.GetComponentName( i ) for i in range( 3 ) )
-
-        assert ( vnp.vtk_to_numpy( createdAttribute ) == np.full( ( elementSize[ iter.GetCurrentFlatIndex() - 1 ], 3 ),
-                                                                  fill_value=values ) ).all()
-        assert cnames == componentNames
+            dataRef = datasetRef.GetCellData()
+            dataTest = datasetTest.GetCellData()
 
-        iter.GoToNextItem()
-
-
-@pytest.mark.parametrize( "values, onpoints, elementSize", [
-    ( ( 42, 58, -103 ), True, 4092 ),
-    ( ( -42, -58, 103 ), False, 1740 ),
-] )
+        attributeRef: int = dataRef.HasArray( attributeName )
+        attributeTest: int = dataTest.HasArray( attributeName )
+        if isNewOnBlock[ idBlock ]:
+            assert attributeRef != attributeTest
+        else:
+            assert attributeRef == attributeTest
+
+
+@pytest.mark.parametrize(
+    "values, componentNames, componentNamesTest, onPoints, vtkDataType, vtkDataTypeTest, valueType", [
+        ( [ np.float32( 42 ) ], (), (), True, VTK_FLOAT, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ) ], (), (), False, VTK_FLOAT, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ) ], (), (), True, None, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ) ], (), (), False, None, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], (),
+          ( "Component0", "Component1" ), True, VTK_FLOAT, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], (),
+          ( "Component0", "Component1" ), False, VTK_FLOAT, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], (),
+          ( "Component0", "Component1" ), True, None, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], (),
+          ( "Component0", "Component1" ), False, None, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), True, VTK_FLOAT, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), False, VTK_FLOAT, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), True, None, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), False, None, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], ( "X", "Y", "Z" ),
+          ( "X", "Y" ), True, VTK_FLOAT, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], ( "X", "Y", "Z" ),
+          ( "X", "Y" ), False, VTK_FLOAT, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], ( "X", "Y", "Z" ), ( "X", "Y" ), True, None, VTK_FLOAT, "float32" ),
+        ( [ np.float32( 42 ), np.float32( 22 ) ], ( "X", "Y", "Z" ), ( "X", "Y" ), False, None, VTK_FLOAT, "float32" ),
+        ( [ np.float64( 42 ) ], (), (), True, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ) ], (), (), False, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ) ], (), (), True, None, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ) ], (), (), False, None, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], (),
+          ( "Component0", "Component1" ), True, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], (),
+          ( "Component0", "Component1" ), False, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], (),
+          ( "Component0", "Component1" ), True, None, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], (),
+          ( "Component0", "Component1" ), False, None, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), True, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], ( "X", "Y" ),
+          ( "X", "Y" ), False, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), True, None, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), False, None, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], ( "X", "Y", "Z" ),
+          ( "X", "Y" ), True, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], ( "X", "Y", "Z" ),
+          ( "X", "Y" ), False, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], ( "X", "Y", "Z" ), ( "X", "Y" ), True, None, VTK_DOUBLE, "float64" ),
+        ( [ np.float64( 42 ), np.float64( 22 ) ], ( "X", "Y", "Z" ), ( "X", "Y" ), False, None, VTK_DOUBLE, "float64" ),
+        ( [ np.int32( 42 ) ], (), (), True, VTK_INT, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ) ], (), (), False, VTK_INT, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ) ], (), (), True, None, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ) ], (), (), False, None, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], (), ( "Component0", "Component1" ), True, VTK_INT, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], (), ( "Component0", "Component1" ), False, VTK_INT, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], (), ( "Component0", "Component1" ), True, None, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], (), ( "Component0", "Component1" ), False, None, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), True, VTK_INT, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), False, VTK_INT, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), True, None, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), False, None, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], ( "X", "Y", "Z" ), ( "X", "Y" ), True, VTK_INT, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], ( "X", "Y", "Z" ), ( "X", "Y" ), False, VTK_INT, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], ( "X", "Y", "Z" ), ( "X", "Y" ), True, None, VTK_INT, "int32" ),
+        ( [ np.int32( 42 ), np.int32( 22 ) ], ( "X", "Y", "Z" ), ( "X", "Y" ), False, None, VTK_INT, "int32" ),
+        ( [ np.int64( 42 ) ], (), (), True, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+        ( [ np.int64( 42 ) ], (), (), False, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+        ( [ np.int64( 42 ) ], (), (), True, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ) ], (), (), False, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ) ], (), (), True, None, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ) ], (), (), False, None, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], (),
+          ( "Component0", "Component1" ), True, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], (),
+          ( "Component0", "Component1" ), False, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], (),
+          ( "Component0", "Component1" ), True, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], (),
+          ( "Component0", "Component1" ), False, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], (), ( "Component0", "Component1" ), True, None, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], (), ( "Component0", "Component1" ), False, None, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), True, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), False, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), True, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y" ),
+          ( "X", "Y" ), False, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), True, None, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y" ), ( "X", "Y" ), False, None, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y", "Z" ),
+          ( "X", "Y" ), True, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y", "Z" ),
+          ( "X", "Y" ), False, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y", "Z" ),
+          ( "X", "Y" ), True, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y", "Z" ),
+          ( "X", "Y" ), False, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y", "Z" ), ( "X", "Y" ), True, None, VTK_LONG_LONG, "int64" ),
+        ( [ np.int64( 42 ), np.int64( 22 ) ], ( "X", "Y", "Z" ), ( "X", "Y" ), False, None, VTK_LONG_LONG, "int64" ),
+    ] )
 def test_createConstantAttributeDataSet(
     dataSetTest: vtkDataSet,
-    values: list[ float ],
-    elementSize: int,
-    onpoints: bool,
+    values: list[ Any ],
+    componentNames: tuple[ str, ...],
+    componentNamesTest: tuple[ str, ...],
+    onPoints: bool,
+    vtkDataType: Union[ int, Any ],
+    vtkDataTypeTest: int,
+    valueType: str,
 ) -> None:
     """Test constant attribute creation in dataset."""
-    vtkDataSetTest: vtkDataSet = dataSetTest( "dataset" )
-    componentNames: Tuple[ str, str, str ] = ( "XX", "YY", "ZZ" )
+    dataSet: vtkDataSet = dataSetTest( "dataset" )
     attributeName: str = "newAttributedataset"
-    arrayModifiers.createConstantAttributeDataSet( vtkDataSetTest, values, attributeName, componentNames, onpoints )
+    arrayModifiers.createConstantAttributeDataSet( dataSet, values, attributeName, componentNames, onPoints,
+                                                   vtkDataType )
 
     data: Union[ vtkPointData, vtkCellData ]
-    if onpoints:
-        data = vtkDataSetTest.GetPointData()
-
+    nbElements: int
+    if onPoints:
+        data = dataSet.GetPointData()
+        nbElements = dataSet.GetNumberOfPoints()
     else:
-        data = vtkDataSetTest.GetCellData()
+        data = dataSet.GetCellData()
+        nbElements = dataSet.GetNumberOfCells()
 
-    createdAttribute: vtkDoubleArray = data.GetArray( attributeName )
-    cnames: Tuple[ str, ...] = tuple( createdAttribute.GetComponentName( i ) for i in range( 3 ) )
+    createdAttribute: vtkDataArray = data.GetArray( attributeName )
 
-    assert ( vnp.vtk_to_numpy( createdAttribute ) == np.full( ( elementSize, 3 ), fill_value=values ) ).all()
-    assert cnames == componentNames
+    nbComponents: int = len( values )
+    nbComponentsCreated: int = createdAttribute.GetNumberOfComponents()
+    assert nbComponents == nbComponentsCreated
 
+    npArray: npt.NDArray[ Any ]
+    if nbComponents > 1:
+        componentNamesCreated: tuple[ str, ...] = tuple(
+            createdAttribute.GetComponentName( i ) for i in range( nbComponents ) )
+        assert componentNamesTest == componentNamesCreated
 
-@pytest.mark.parametrize( "onpoints, arrayTest, arrayExpected", [
-    ( True, 4092, "random_4092" ),
-    ( False, 1740, "random_1740" ),
-],
-                          indirect=[ "arrayTest", "arrayExpected" ] )
+        npArray = np.array( [ values for _ in range( nbElements ) ] )
+    else:
+        npArray = np.array( [ values[ 0 ] for _ in range( nbElements ) ] )
+
+    npArraycreated: npt.NDArray[ Any ] = vnp.vtk_to_numpy( createdAttribute )
+    assert ( npArray == npArraycreated ).all()
+    assert valueType == npArraycreated.dtype
+
+    vtkDataTypeCreated: int = createdAttribute.GetDataType()
+    assert vtkDataTypeTest == vtkDataTypeCreated
+
+
+@pytest.mark.parametrize( "componentNames, componentNamesTest, onPoints, vtkDataType, vtkDataTypeTest, valueType", [
+    ( (), (), True, VTK_FLOAT, VTK_FLOAT, "float32" ),
+    ( (), (), False, VTK_FLOAT, VTK_FLOAT, "float32" ),
+    ( (), (), True, None, VTK_FLOAT, "float32" ),
+    ( (), (), False, None, VTK_FLOAT, "float32" ),
+    ( (), ( "Component0", "Component1" ), True, VTK_FLOAT, VTK_FLOAT, "float32" ),
+    ( (), ( "Component0", "Component1" ), False, VTK_FLOAT, VTK_FLOAT, "float32" ),
+    ( (), ( "Component0", "Component1" ), True, None, VTK_FLOAT, "float32" ),
+    ( (), ( "Component0", "Component1" ), False, None, VTK_FLOAT, "float32" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), True, VTK_FLOAT, VTK_FLOAT, "float32" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), False, VTK_FLOAT, VTK_FLOAT, "float32" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), True, None, VTK_FLOAT, "float32" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), False, None, VTK_FLOAT, "float32" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), True, VTK_FLOAT, VTK_FLOAT, "float32" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), False, VTK_FLOAT, VTK_FLOAT, "float32" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), True, None, VTK_FLOAT, "float32" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), False, None, VTK_FLOAT, "float32" ),
+    ( (), (), True, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+    ( (), (), False, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+    ( (), (), True, None, VTK_DOUBLE, "float64" ),
+    ( (), (), False, None, VTK_DOUBLE, "float64" ),
+    ( (), ( "Component0", "Component1" ), True, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+    ( (), ( "Component0", "Component1" ), False, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+    ( (), ( "Component0", "Component1" ), True, None, VTK_DOUBLE, "float64" ),
+    ( (), ( "Component0", "Component1" ), False, None, VTK_DOUBLE, "float64" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), True, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), False, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), True, None, VTK_DOUBLE, "float64" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), False, None, VTK_DOUBLE, "float64" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), True, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), False, VTK_DOUBLE, VTK_DOUBLE, "float64" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), True, None, VTK_DOUBLE, "float64" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), False, None, VTK_DOUBLE, "float64" ),
+    ( (), (), True, VTK_INT, VTK_INT, "int32" ),
+    ( (), (), False, VTK_INT, VTK_INT, "int32" ),
+    ( (), (), True, None, VTK_INT, "int32" ),
+    ( (), (), False, None, VTK_INT, "int32" ),
+    ( (), ( "Component0", "Component1" ), True, VTK_INT, VTK_INT, "int32" ),
+    ( (), ( "Component0", "Component1" ), False, VTK_INT, VTK_INT, "int32" ),
+    ( (), ( "Component0", "Component1" ), True, None, VTK_INT, "int32" ),
+    ( (), ( "Component0", "Component1" ), False, None, VTK_INT, "int32" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), True, VTK_INT, VTK_INT, "int32" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), False, VTK_INT, VTK_INT, "int32" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), True, None, VTK_INT, "int32" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), False, None, VTK_INT, "int32" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), True, VTK_INT, VTK_INT, "int32" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), False, VTK_INT, VTK_INT, "int32" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), True, None, VTK_INT, "int32" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), False, None, VTK_INT, "int32" ),
+    ( (), (), True, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+    ( (), (), False, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+    ( (), (), True, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+    ( (), (), False, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+    ( (), (), True, None, VTK_LONG_LONG, "int64" ),
+    ( (), (), False, None, VTK_LONG_LONG, "int64" ),
+    ( (), ( "Component0", "Component1" ), True, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+    ( (), ( "Component0", "Component1" ), False, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+    ( (), ( "Component0", "Component1" ), True, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+    ( (), ( "Component0", "Component1" ), False, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+    ( (), ( "Component0", "Component1" ), True, None, VTK_LONG_LONG, "int64" ),
+    ( (), ( "Component0", "Component1" ), False, None, VTK_LONG_LONG, "int64" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), True, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), False, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), True, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), False, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), True, None, VTK_LONG_LONG, "int64" ),
+    ( ( "X", "Y" ), ( "X", "Y" ), False, None, VTK_LONG_LONG, "int64" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), True, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), False, VTK_ID_TYPE, VTK_ID_TYPE, "int64" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), True, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), False, VTK_LONG_LONG, VTK_LONG_LONG, "int64" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), True, None, VTK_LONG_LONG, "int64" ),
+    ( ( "X", "Y", "Z" ), ( "X", "Y" ), False, None, VTK_LONG_LONG, "int64" ),
+] )
 def test_createAttribute(
     dataSetTest: vtkDataSet,
-    arrayTest: npt.NDArray[ np.float64 ],
-    arrayExpected: npt.NDArray[ np.float64 ],
-    onpoints: bool,
+    getArrayWithSpeTypeValue: npt.NDArray[ Any ],
+    componentNames: tuple[ str, ...],
+    componentNamesTest: tuple[ str, ...],
+    onPoints: bool,
+    vtkDataType: int,
+    vtkDataTypeTest: int,
+    valueType: str,
 ) -> None:
     """Test creation of dataset in dataset from given array."""
-    vtkDataSetTest: vtkDataSet = dataSetTest( "dataset" )
-    componentNames: tuple[ str, str, str ] = ( "XX", "YY", "ZZ" )
+    dataSet: vtkDataSet = dataSetTest( "dataset" )
     attributeName: str = "AttributeName"
 
-    arrayModifiers.createAttribute( vtkDataSetTest, arrayTest, attributeName, componentNames, onpoints )
+    nbComponents: int = ( 1 if len( componentNamesTest ) == 0 else len( componentNamesTest ) )
+    nbElements: int = ( dataSet.GetNumberOfPoints() if onPoints else dataSet.GetNumberOfCells() )
 
-    data: Union[ vtkPointData, vtkCellData ]
-    if onpoints:
-        data = vtkDataSetTest.GetPointData()
-    else:
-        data = vtkDataSetTest.GetCellData()
+    npArray: npt.NDArray[ Any ] = getArrayWithSpeTypeValue( nbComponents, nbElements, valueType )
+    arrayModifiers.createAttribute( dataSet, npArray, attributeName, componentNames, onPoints, vtkDataType )
 
-    createdAttribute: vtkDoubleArray = data.GetArray( attributeName )
-    cnames: Tuple[ str, ...] = tuple( createdAttribute.GetComponentName( i ) for i in range( 3 ) )
-
-    assert ( vnp.vtk_to_numpy( createdAttribute ) == arrayExpected ).all()
-    assert cnames == componentNames
-
-
-def test_copyAttribute( dataSetTest: vtkMultiBlockDataSet ) -> None:
+    data: Union[ vtkPointData, vtkCellData ]
+    data = dataSet.GetPointData() if onPoints else dataSet.GetCellData()
+
+    createdAttribute: vtkDataArray = data.GetArray( attributeName )
+    nbComponentsCreated: int = createdAttribute.GetNumberOfComponents()
+    assert nbComponents == nbComponentsCreated
+    if nbComponents > 1:
+        componentsNamesCreated: tuple[ str, ...] = tuple(
+            createdAttribute.GetComponentName( i ) for i in range( nbComponents ) )
+        assert componentNamesTest == componentsNamesCreated
+
+    npArraycreated: npt.NDArray[ Any ] = vnp.vtk_to_numpy( createdAttribute )
+    assert ( npArray == npArraycreated ).all()
+    assert valueType == npArraycreated.dtype
+
+    vtkDataTypeCreated: int = createdAttribute.GetDataType()
+    assert vtkDataTypeTest == vtkDataTypeCreated
+
+
+@pytest.mark.parametrize( "attributeNameFrom, attributeNameTo, onPoints, idBlock", [
+    ( "PORO", "POROTo", False, 0 ),
+    ( "CellAttribute", "CellAttributeTo", False, 0 ),
+    ( "FAULT", "FAULTTo", False, 0 ),
+    ( "PointAttribute", "PointAttributeTo", True, 0 ),
+    ( "collocated_nodes", "collocated_nodesTo", True, 1 ),
+] )
+def test_copyAttribute( dataSetTest: vtkMultiBlockDataSet, attributeNameFrom: str, attributeNameTo: str, onPoints: bool,
+                        idBlock: int ) -> None:
     """Test copy of cell attribute from one multiblock to another."""
     objectFrom: vtkMultiBlockDataSet = dataSetTest( "multiblock" )
-    objectTo: vtkMultiBlockDataSet = dataSetTest( "multiblock" )
+    objectTo: vtkMultiBlockDataSet = dataSetTest( "emptymultiblock" )
 
-    attributeFrom: str = "CellAttribute"
-    attributeTo: str = "CellAttributeTO"
+    arrayModifiers.copyAttribute( objectFrom, objectTo, attributeNameFrom, attributeNameTo, onPoints )
 
-    arrayModifiers.copyAttribute( objectFrom, objectTo, attributeFrom, attributeTo )
+    blockFrom: vtkDataSet = cast( vtkDataSet, objectFrom.GetBlock( idBlock ) )
+    blockTo: vtkDataSet = cast( vtkDataSet, objectTo.GetBlock( idBlock ) )
 
-    blockIndex: int = 0
-    blockFrom: vtkDataSet = cast( vtkDataSet, objectFrom.GetBlock( blockIndex ) )
-    blockTo: vtkDataSet = cast( vtkDataSet, objectTo.GetBlock( blockIndex ) )
+    dataFrom: Union[ vtkPointData, vtkCellData ]
+    dataTo: Union[ vtkPointData, vtkCellData ]
+    if onPoints:
+        dataFrom = blockFrom.GetPointData()
+        dataTo = blockTo.GetPointData()
+    else:
+        dataFrom = blockFrom.GetCellData()
+        dataTo = blockTo.GetCellData()
 
-    arrayFrom: npt.NDArray[ np.float64 ] = vnp.vtk_to_numpy( blockFrom.GetCellData().GetArray( attributeFrom ) )
-    arrayTo: npt.NDArray[ np.float64 ] = vnp.vtk_to_numpy( blockTo.GetCellData().GetArray( attributeTo ) )
+    attributeFrom: vtkDataArray = dataFrom.GetArray( attributeNameFrom )
+    attributeTo: vtkDataArray = dataTo.GetArray( attributeNameTo )
 
-    assert ( arrayFrom == arrayTo ).all()
+    nbComponentsFrom: int = attributeFrom.GetNumberOfComponents()
+    nbComponentsTo: int = attributeTo.GetNumberOfComponents()
+    assert nbComponentsFrom == nbComponentsTo
 
+    if nbComponentsFrom > 1:
+        componentsNamesFrom: tuple[ str, ...] = tuple(
+            attributeFrom.GetComponentName( i ) for i in range( nbComponentsFrom ) )
+        componentsNamesTo: tuple[ str,
+                                  ...] = tuple( attributeTo.GetComponentName( i ) for i in range( nbComponentsTo ) )
+        assert componentsNamesFrom == componentsNamesTo
 
-def test_copyAttributeDataSet( dataSetTest: vtkDataSet, ) -> None:
-    """Test copy of cell attribute from one dataset to another."""
-    objectFrom: vtkDataSet = dataSetTest( "dataset" )
-    objectTo: vtkDataSet = dataSetTest( "dataset" )
+    npArrayFrom: npt.NDArray[ Any ] = vnp.vtk_to_numpy( attributeFrom )
+    npArrayTo: npt.NDArray[ Any ] = vnp.vtk_to_numpy( attributeTo )
+    assert ( npArrayFrom == npArrayTo ).all()
+    assert npArrayFrom.dtype == npArrayTo.dtype
 
-    attributNameFrom = "CellAttribute"
-    attributNameTo = "COPYATTRIBUTETO"
+    vtkDataTypeFrom: int = attributeFrom.GetDataType()
+    vtkDataTypeTo: int = attributeTo.GetDataType()
+    assert vtkDataTypeFrom == vtkDataTypeTo
 
-    arrayModifiers.copyAttributeDataSet( objectFrom, objectTo, attributNameFrom, attributNameTo )
 
-    arrayFrom: npt.NDArray[ np.float64 ] = vnp.vtk_to_numpy( objectFrom.GetCellData().GetArray( attributNameFrom ) )
-    arrayTo: npt.NDArray[ np.float64 ] = vnp.vtk_to_numpy( objectTo.GetCellData().GetArray( attributNameTo ) )
+@pytest.mark.parametrize( "attributeNameFrom, attributeNameTo, onPoints", [
+    ( "CellAttribute", "CellAttributeTo", False ),
+    ( "PointAttribute", "PointAttributeTo", True ),
+] )
+def test_copyAttributeDataSet( dataSetTest: vtkDataSet, attributeNameFrom: str, attributeNameTo: str,
+                               onPoints: bool ) -> None:
+    """Test copy of an attribute from one dataset to another."""
+    objectFrom: vtkDataSet = dataSetTest( "dataset" )
+    objectTo: vtkDataSet = dataSetTest( "emptydataset" )
 
-    assert ( arrayFrom == arrayTo ).all()
+    arrayModifiers.copyAttributeDataSet( objectFrom, objectTo, attributeNameFrom, attributeNameTo, onPoints )
+
+    dataFrom: Union[ vtkPointData, vtkCellData ]
+    dataTo: Union[ vtkPointData, vtkCellData ]
+    if onPoints:
+        dataFrom = objectFrom.GetPointData()
+        dataTo = objectTo.GetPointData()
+    else:
+        dataFrom = objectFrom.GetCellData()
+        dataTo = objectTo.GetCellData()
+
+    attributeFrom: vtkDataArray = dataFrom.GetArray( attributeNameFrom )
+    attributeTo: vtkDataArray = dataTo.GetArray( attributeNameTo )
+
+    nbComponentsFrom: int = attributeFrom.GetNumberOfComponents()
+    nbComponentsTo: int = attributeTo.GetNumberOfComponents()
+    assert nbComponentsFrom == nbComponentsTo
+
+    if nbComponentsFrom > 1:
+        componentsNamesFrom: tuple[ str, ...] = tuple(
+            attributeFrom.GetComponentName( i ) for i in range( nbComponentsFrom ) )
+        componentsNamesTo: tuple[ str,
+                                  ...] = tuple( attributeTo.GetComponentName( i ) for i in range( nbComponentsTo ) )
+        assert componentsNamesFrom == componentsNamesTo
+
+    vtkDataTypeFrom: int = attributeFrom.GetDataType()
+    vtkDataTypeTo: int = attributeTo.GetDataType()
+    assert vtkDataTypeFrom == vtkDataTypeTo
+
+    npArrayFrom: npt.NDArray[ Any ] = vnp.vtk_to_numpy( attributeFrom )
+    npArrayTo: npt.NDArray[ Any ] = vnp.vtk_to_numpy( attributeTo )
+    assert ( npArrayFrom == npArrayTo ).all()
+    assert npArrayFrom.dtype == npArrayTo.dtype
 
 
 @pytest.mark.parametrize( "attributeName, onpoints", [
diff --git a/geos-posp/src/PVplugins/PVAttributeMapping.py b/geos-posp/src/PVplugins/PVAttributeMapping.py
index a862b9a9..39b17b51 100644
--- a/geos-posp/src/PVplugins/PVAttributeMapping.py
+++ b/geos-posp/src/PVplugins/PVAttributeMapping.py
@@ -21,9 +21,7 @@
 from geos.mesh.utils.arrayModifiers import fillPartialAttributes
 from geos.mesh.utils.multiblockModifiers import mergeBlocks
 from geos.mesh.utils.arrayHelpers import (
-    getAttributeSet,
-    getNumberOfComponents,
-)
+    getAttributeSet, )
 from geos_posp.visu.PVUtils.checkboxFunction import (  # type: ignore[attr-defined]
     createModifiedCallback, )
 from geos_posp.visu.PVUtils.paraviewTreatments import getArrayChoices
@@ -192,8 +190,7 @@ def RequestData(
             outData.ShallowCopy( clientMesh )
             attributeNames: set[ str ] = set( getArrayChoices( self.a02GetAttributeToTransfer() ) )
             for attributeName in attributeNames:
-                nbComponents = getNumberOfComponents( serverMesh, attributeName, False )
-                fillPartialAttributes( serverMesh, attributeName, nbComponents, False )
+                fillPartialAttributes( serverMesh, attributeName, False )
 
             mergedServerMesh: vtkUnstructuredGrid
             if isinstance( serverMesh, vtkUnstructuredGrid ):
diff --git a/geos-posp/src/geos_posp/filters/GeosBlockMerge.py b/geos-posp/src/geos_posp/filters/GeosBlockMerge.py
index 09b0a879..0844b1e8 100644
--- a/geos-posp/src/geos_posp/filters/GeosBlockMerge.py
+++ b/geos-posp/src/geos_posp/filters/GeosBlockMerge.py
@@ -365,14 +365,9 @@ def mergeChildBlocks( self: Self, compositeBlock: vtkMultiBlockDataSet ) -> vtkU
         Returns:
             vtkUnstructuredGrid: merged block
         """
-        # fill partial cell attributes in all children blocks
-        if not fillAllPartialAttributes( compositeBlock, False ):
-            self.m_logger.warning( "Some partial cell attributes may not have been " + "propagated to the whole mesh." )
-
-        # # fill partial point attributes in all children blocks
-        if not fillAllPartialAttributes( compositeBlock, True ):
-            self.m_logger.warning( "Some partial point attributes may not have been " +
-                                   "propagated to the whole mesh." )
+        # fill partial attributes in all children blocks
+        if not fillAllPartialAttributes( compositeBlock ):
+            self.m_logger.warning( "Some partial attributes may not have been " + "propagated to the whole mesh." )
 
         # merge blocks
         return mergeBlocks( compositeBlock )