Load custom data in DPF#

This tutorial shows how to represent your custom data in DPF data storage structures.

To import you custom data in DPF, you must create a DPF data structure to store it. DPF uses Field and FieldsContainer objects to handle data. The Field is a homogeneous array and a FieldsContainer is a labeled collection of Field. For more information on DPF data structures such as the Field and their use check the DPF data structures tutorials section.

Download tutorial as Python script Download tutorial as Jupyter notebook

Define the data#

In this tutorial, we create different Fields from data stored in Python lists.

Create the python lists with the data to be set to the Fields.

# Data for the scalar Fields (lists with 1 and 2 dimensions)
data_1 = [6.0, 5.0, 4.0, 3.0, 2.0, 1.0]
data_2 = [[12.0, 7.0, 8.0], [ 9.0, 31.0, 1.0]]

# Data for the vector Fields (lists with 1 and 2 dimensions)
data_3 = [4.0, 1.0, 8.0, 5.0, 7.0, 9.0]
data_4 = [6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 9.0, 7.0, 8.0, 10.0]
data_5 = [[8.0, 4.0, 3.0], [31.0, 5.0, 7.0]]

# Data for the matrix Fields
data_6 = [3.0, 2.0, 1.0, 7.0]
data_7 = [15.0, 3.0, 9.0, 31.0, 1.0, 42.0, 5.0, 68.0, 13.0]
data_8 = [[12.0, 7.0, 8.0], [ 1.0, 4.0, 27.0], [98.0, 4.0, 6.0]]

Create the python lists with the data to be appended to the Fields.

# Data for the scalar Fields
data_9 = [24.0]

# Data for the vector Fields
data_10 = [47.0, 33.0, 5.0]

# Data for the matrix Fields
data_11 = [8.0, 2.0, 4.0, 64.0, 32.0, 47.0, 11.0, 23.0, 1.0]

Create the Fields#

In this tutorial, we explain how to create the following Fields:

  • Scalar Field;

  • Vector Field;

  • Matrix Field.

Note

A Field must always be given:

  • A location and a Scoping.

    Here, we create Fields in the default ‘Nodal’ location. Thus, each entity (here, the nodes) must have a Scoping id, that can be defined in a random or in a numerical order:

    • If you want to set a data array to the Field, you must previously set the Scoping ids using the Field.scoping method.

    • If you want to append an entity with a data array to the Field, you don’t need to previously set the Scoping ids.

  • A nature and a dimensionality (number of data components for each entity). They must respect the type and size of the data to be stored in the Field.

Import the PyDPF-Core library#

First, import the PyDPF-Core library.

# Import the ``ansys.dpf.core`` module
from ansys.dpf import core as dpf

Define the Fields sizing#

The second step consists in defining the Fields dimensions.

Here, we create one Field with 6 scalar. Thus, 6 entities with one Scoping id each.

# Define the number of entities
num_entities_1 = 6

You must ensure that this Field has a ‘scalar’ nature and an ‘1D’ dimensionality.

Here, we create:

  • One Field with 2 vectors (thus, 2 entities) of 3 components each (3D vector Field);

  • One Field with 2 vectors (thus, 2 entities) of 5 components each (5D vector Field);

# Define the number of entities
num_entities_2 = 2

You must ensure that these Fields have a ‘vector’ nature and the corresponding dimensionality (‘3D’ and ‘5D’).

Here, we create:

  • One Field with 1 matrix (thus, 1 entity) of 2 lines and 2 columns;

  • Two Fields with 1 matrix (thus, 1 entity) of 3 lines and 3 columns (tensor).

# Define the number of entities
num_entities_3 = 1

You must ensure that these Fields have a ‘matrix’ nature and the corresponding dimensionality.

Create the Fields objects#

You can create the Fields using two approaches:

Create a Field by an instance of this object#

# Define the number of entities
num_entities_1 = 6

You must ensure that this Field has a ‘scalar’ nature and an ‘1D’ dimensionality.

For this approach, the default nature of the Field object is ‘vector’. You can modify it directly with the ‘nature’ argument or with the Field.dimensionality method.

Create the scalar Field and use the ‘nature’ argument.

# Instanciate the Field
field_11 = dpf.Field(nentities=num_entities_1, nature=dpf.common.natures.scalar)

# Set the scoping ids
field_11.scoping.ids = range(num_entities_1)

# Print the Field
print("Scalar Field: ", '\n',field_11, '\n')

Scalar Field:  
 DPF  Field
  Location: Nodal
  Unit: 
  6 entities 
  Data: 1 components and 0 elementary data

Create the scalar Field and use the Field.dimensionality method.

# Instanciate the Field
field_12 = dpf.Field(nentities=num_entities_1)

# Use the Field.dimensionality method
field_12.dimensionality = dpf.Dimensionality([1])

# Set the scoping ids
field_12.scoping.ids = range(num_entities_1)

# Print the Field
print("Scalar Field : ", '\n',field_12, '\n')

Scalar Field :  
 DPF  Field
  Location: Nodal
  Unit: 
  6 entities 
  Data: 1 components and 0 elementary data

Here, we create:

  • One Field with 2 vectors (thus, 2 entities) of 3 components each (3D vector Field);

  • One Field with 2 vectors (thus, 2 entities) of 5 components each (5D vector Field);

# Define the number of entities
num_entities_2 = 2

You must ensure that these Fields have a ‘vector’ nature and the corresponding dimensionality (‘3D’ and ‘5D’).

For this approach, the default nature is ‘vector’ and the default dimensionality is ‘3D’. So for the second vector Field you must set a ‘5D’ dimensionality using the Field.dimensionality method.

Create the ‘3D’ vector Field.

# Instantiate the Field
field_21 = dpf.Field(nentities=num_entities_2)

# Set the scoping ids
field_21.scoping.ids = range(num_entities_2)

# Print the Field
print("3D vector Field : ", '\n',field_21, '\n')

3D vector Field :  
 DPF  Field
  Location: Nodal
  Unit: 
  2 entities 
  Data: 3 components and 0 elementary data

Create the ‘5D’ vector Field.

# Instantiate the Field
field_31 = dpf.Field(nentities=num_entities_2)

# Use the Field.dimensionality method
field_31.dimensionality = dpf.Dimensionality([5])

# Set the scoping ids
field_31.scoping.ids = range(num_entities_2)

# Print the Field
print("5D vector Field (5D): ", '\n',field_31, '\n')

5D vector Field (5D):  
 DPF  Field
  Location: Nodal
  Unit: 
  2 entities 
  Data: 5 components and 0 elementary data

Create a Field using the fields_factory module#

You can use two functions from the fields_factory module to create a scalar Field:

  • The create_scalar_field() function;

  • The field_from_array() function.

Create the Field using the create_scalar_field function

For this approach, the default nature of the Field object is ‘scalar’ and the default dimensionality is ‘1D’. Thus, you just have to use the create_scalar_field() function to create a scalar Field.

# Create the scalar Field
field_13 = dpf.fields_factory.create_scalar_field(num_entities=num_entities_1)

# Set the scoping ids
field_13.scoping.ids = range(num_entities_1)

# Print the Field
print("Scalar Field: ", '\n',field_13, '\n')

Scalar Field:  
 DPF  Field
  Location: Nodal
  Unit: 
  6 entities 
  Data: 1 components and 0 elementary data

Create the Field using the field_from_array function

Different from the other approaches, where you set or append the data after creating the Field, here, the data is used as an input of the field_from_array() function.

This function gets an Numpy array or Python list of either:

  • 1 dimension (one array). In this case, you get directly a scalar Field;

  • 2 dimensions (one array containing multiple arrays with 3 components each). In the is case, you get a 3D vector Field. Thus, you have to change the Field dimensionality using the Field.dimensionality method.

Create the scalar Field with an 1 dimensional list.

# Use the field_from_array function
field_14 = dpf.fields_factory.field_from_array(arr=data_1)

# Set the scoping ids
field_14.scoping.ids = range(num_entities_1)

# Print the Field
print("Scalar Field: ", '\n',field_14, '\n')

Scalar Field:  
 DPF  Field
  Location: Nodal
  Unit: 
  6 entities 
  Data: 1 components and 6 elementary data 

  IDs                   data
  ------------          ----------
  0                     6.000000e+00   
                        
  1                     5.000000e+00   
                        
  2                     4.000000e+00   
                        
  ...

Create the scalar Field with a 2 dimensional list.

# Use the field_from_array function
field_15 = dpf.fields_factory.field_from_array(arr=data_2)

# Use the |Field.dimensionality| method
field_15.dimensionality = dpf.Dimensionality([1])

# Set the scoping ids
field_15.scoping.ids = range(num_entities_1)

# Print the Field
print("Scalar Field (b): ", '\n',field_15, '\n')

Scalar Field (b):  
 DPF  Field
  Location: Nodal
  Unit: 
  6 entities 
  Data: 1 components and 6 elementary data 

  IDs                   data
  ------------          ----------
  0                     1.200000e+01   
                        
  1                     7.000000e+00   
                        
  2                     8.000000e+00   
                        
  ...

You can use three functions from the fields_factory module to create a vector Field:

  • The create_vector_field() function;

  • The create_3d_vector_field() function (Specifically to create a 3D vector Field (a vector Field with 3 components for each entity));

  • The field_from_array() function.

Create the Field using the create_vector_field() function

For this approach, the default nature is ‘vector’. To define the dimensionality you must use the ‘num_comp’ argument.

Create the ‘3D’ vector Field.

# Use the create_vector_field function
field_22 = dpf.fields_factory.create_vector_field(num_entities=num_entities_2, num_comp=3)

# Set the scoping ids
field_22.scoping.ids = range(num_entities_2)

# Print the Field
print("3D vector Field : ", '\n',field_22, '\n')

3D vector Field :  
 DPF  Field
  Location: Nodal
  Unit: 
  2 entities 
  Data: 3 components and 0 elementary data

Create the ‘5D’ vector Field.

# Use the create_vector_field function
field_32 = dpf.fields_factory.create_vector_field(num_entities=num_entities_2, num_comp=5)

# Set the scoping ids
field_32.scoping.ids = range(num_entities_2)

# Print the Field
print("5D vector Field : ", '\n',field_32, '\n')

5D vector Field :  
 DPF  Field
  Location: Nodal
  Unit: 
  2 entities 
  Data: 5 components and 0 elementary data

Create a 3d vector Field using the create_3d_vector_field() function

For this approach, the default nature is ‘vector’ and the dimensionality is ‘3D’. Thus, you just have to use the create_3d_vector_field() function to create a 3D vector Field.

# Create the 3d vector Field
field_25 = dpf.fields_factory.create_3d_vector_field(num_entities=num_entities_2)
# Set the scoping ids
field_25.scoping.ids = range(num_entities_2)

# Print the Field
print("Vector Field (3D): ", '\n',field_25, '\n')

Vector Field (3D):  
 DPF  Field
  Location: Nodal
  Unit: 
  2 entities 
  Data: 3 components and 0 elementary data

Create the Field using the field_from_array() function

Different from the other approaches, where you set or append the data after creating the Field, here, the data is used as an input of the field_from_array() function.

This function gets an Numpy array or Python list of either:

  • 1 dimension (one array). In this case, you have to change the Field dimensionality using the Field.dimensionality method.

  • 2 dimensions (one array containing multiple arrays with 3 components). In the is case, you get a 3D vector Field.

Note

The Field must always assure a homogeneous shape. The shape is a tuple with the number of elementary data and the number of components.

So, for the ‘5D vector Field we would want a shape of (10,5). Nevertheless, the 2 dimensions data vector we defined (“data_5”) has a elementary data count of 6 (2*3). Thus, we cannot define the ‘5D’ vector Field because it would have a (6,5) shape.

Create the ‘3D’ vector Field with an 1 dimensional list.

# Use the field_from_array function
field_23 = dpf.fields_factory.field_from_array(arr=data_3)

# Use the Field.dimensionality method
field_23.dimensionality = dpf.Dimensionality([3])

# Set the scoping ids
field_23.scoping.ids = range(num_entities_2)

# Print the Field
print("3D vector Field: ", '\n',field_23, '\n')

3D vector Field:  
 DPF  Field
  Location: Nodal
  Unit: 
  2 entities 
  Data: 3 components and 2 elementary data 

  IDs                   data
  ------------          ----------
  0                     4.000000e+00   1.000000e+00   8.000000e+00   
                        
  1                     5.000000e+00   7.000000e+00   9.000000e+00

Create the ‘3D’ vector Field and give a 2 dimensional list.

# Use the field_from_array function
field_24 = dpf.fields_factory.field_from_array(arr=data_5)

# Set the scoping ids
field_24.scoping.ids = range(num_entities_2)

# Print the Field
print("3D vector Field: ", '\n',field_24, '\n')

3D vector Field:  
 DPF  Field
  Location: Nodal
  Unit: 
  2 entities 
  Data: 3 components and 2 elementary data 

  IDs                   data
  ------------          ----------
  0                     8.000000e+00   4.000000e+00   3.000000e+00   
                        
  1                     3.100000e+01   5.000000e+00   7.000000e+00

You can create a matrix Field using the create_matrix_field() function from the fields_factory module.

The default nature here is ‘matrix’. Thus, you only have to define the matrix dimensionality using the ‘num_lines’ and ‘num_col’ arguments.

Create the (2,2) matrix Field.

# Use the create_matrix_field function
field_41 = dpf.fields_factory.create_matrix_field(num_entities=num_entities_3, num_lines=2, num_col=2)

# Set the scoping ids
field_41.scoping.ids = range(num_entities_3)

# Print the Field
print("Matrix Field (2,2) : ", '\n',field_41, '\n')

Matrix Field (2,2) :  
 DPF  Field
  Location: Nodal
  Unit: 
  1 entities 
  Data: 4 components and 0 elementary data

Create the (3,3) matrix Fields.

# Use the create_matrix_field function
field_51 = dpf.fields_factory.create_matrix_field(num_entities=num_entities_3, num_lines=3, num_col=3)
field_52 = dpf.fields_factory.create_matrix_field(num_entities=num_entities_3, num_lines=3, num_col=3)

# Set the scoping ids
field_51.scoping.ids = range(num_entities_3)
field_52.scoping.ids = range(num_entities_3)

# Print the Field
print("Matrix Field 1 (3,3) : ", '\n',field_51, '\n')
print("Matrix Field 2 (3,3) : ", '\n',field_52, '\n')

Matrix Field 1 (3,3) :  
 DPF  Field
  Location: Nodal
  Unit: 
  1 entities 
  Data: 9 components and 0 elementary data 
 

Matrix Field 2 (3,3) :  
 DPF  Field
  Location: Nodal
  Unit: 
  1 entities 
  Data: 9 components and 0 elementary data

Set data to the Fields#

To set a data array to a Field use the Field.data method. The Field Scoping defines how the data is ordered. For example: the first id in the scoping identifies to which entity the first data entity belongs to.

The data can be in a 1 dimension (one array) or 2 dimensions (one array containing multiple arrays) Numpy array or Python list. When attributed to a Field, these data arrays are reshaped to respect the Field definition.

Set the data from a 1 dimensional array to the scalar Field.

# Set the data
field_11.data = data_1

# Print the Field
print("Scalar Field : ", '\n',field_11, '\n')

# Print the Fields data
print("Data scalar Field : ", '\n',field_11.data, '\n')

Scalar Field :  
 DPF  Field
  Location: Nodal
  Unit: 
  6 entities 
  Data: 1 components and 6 elementary data 

  IDs                   data
  ------------          ----------
  0                     6.000000e+00   
                        
  1                     5.000000e+00   
                        
  2                     4.000000e+00   
                        
  ...

 

Data scalar Field :  
 [6. 5. 4. 3. 2. 1.]

Set the data from a 2 dimensional array to the scalar Field.

# Set the data
field_12.data = data_2

# Print the Field
print("Scalar Field : ", '\n',field_12, '\n')

# Print the Fields data
print("Data scalar Field : ", '\n',field_12.data, '\n')

Scalar Field :  
 DPF  Field
  Location: Nodal
  Unit: 
  6 entities 
  Data: 1 components and 6 elementary data 

  IDs                   data
  ------------          ----------
  0                     1.200000e+01   
                        
  1                     7.000000e+00   
                        
  2                     8.000000e+00   
                        
  ...

 

Data scalar Field :  
 [12.  7.  8.  9. 31.  1.]

Set the data from a 1 dimensional array to the ‘3D’ vector Field.

# Set the data
field_21.data = data_3

# Print the Field
print("Vector Field : ", '\n',field_21, '\n')

# Print the Fields data
print("Data vector Field: ", '\n',field_21.data, '\n')

Vector Field :  
 DPF  Field
  Location: Nodal
  Unit: 
  2 entities 
  Data: 3 components and 2 elementary data 

  IDs                   data
  ------------          ----------
  0                     4.000000e+00   1.000000e+00   8.000000e+00   
                        
  1                     5.000000e+00   7.000000e+00   9.000000e+00   
                        

 

Data vector Field:  
 [[4. 1. 8.]
 [5. 7. 9.]]

Set the data from a 1 dimensional array to the ‘5D’ vector Field.

# Set the data
field_31.data = data_4

# Print the Field
print("Vector Field: ", '\n',field_31, '\n')

# Print the Fields data
print("Data vector Field : ", '\n',field_31.data, '\n')

Vector Field:  
 DPF  Field
  Location: Nodal
  Unit: 
  2 entities 
  Data: 5 components and 2 elementary data 

  IDs                   data
  ------------          ----------
  0                     6.000000e+00   5.000000e+00   4.000000e+00   3.000000e+00   2.000000e+00   
                        
  1                     1.000000e+00   9.000000e+00   7.000000e+00   8.000000e+00   1.000000e+01   
                        

 

Data vector Field :  
 [[ 6.  5.  4.  3.  2.]
 [ 1.  9.  7.  8. 10.]]

Set the data from a 2 dimensional array to the ‘3D’ vector Field.

# Set the data
field_22.data = data_5

# Print the Field
print("Vector Field: ", '\n',field_22, '\n')

# Print the Fields data
print("Data vector Field: ", '\n',field_22.data, '\n')

Vector Field:  
 DPF  Field
  Location: Nodal
  Unit: 
  2 entities 
  Data: 3 components and 2 elementary data 

  IDs                   data
  ------------          ----------
  0                     8.000000e+00   4.000000e+00   3.000000e+00   
                        
  1                     3.100000e+01   5.000000e+00   7.000000e+00   
                        

 

Data vector Field:  
 [[ 8.  4.  3.]
 [31.  5.  7.]]

Set the data from a 1 dimensional array to the (2,2) matrix Field.

# Set the data
field_41.data = data_6

# Print the Field
print("Matrix Field: ", '\n',field_41, '\n')

# Print the Fields data
print("Data matrix Field: ", '\n',field_41.data, '\n')

Matrix Field:  
 DPF  Field
  Location: Nodal
  Unit: 
  1 entities 
  Data: 4 components and 1 elementary data 

  IDs                   data
  ------------          ----------
  0                     3.000000e+00   2.000000e+00   1.000000e+00   7.000000e+00   
                        

 

Data matrix Field:  
 [[3. 2. 1. 7.]]

Set the data from a 1 dimensional array to the (3,3) matrix Field.

# Set the data
field_51.data = data_7

# Print the Field
print("Matrix Field: ", '\n',field_51, '\n')

# Print the Fields data
print("Data matrix Field: ", '\n',field_51.data, '\n')

Matrix Field:  
 DPF  Field
  Location: Nodal
  Unit: 
  1 entities 
  Data: 9 components and 1 elementary data 

  IDs                   data
  ------------          ----------
  0                     1.500000e+01   3.000000e+00   9.000000e+00   3.100000e+01   1.000000e+00   4.200000e+01   5.000000e+00   6.800000e+01   1.300000e+01   
                        

 

Data matrix Field:  
 [[15.  3.  9. 31.  1. 42.  5. 68. 13.]]

Set the data from a 2 dimensional array to the (3,3) matrix Field.

# Set the data
field_52.data = data_8

# Print the Field
print("Matrix Field: ", '\n',field_51, '\n')

# Print the Fields data
print("Data matrix Field: ", '\n',field_51.data, '\n')

Matrix Field:  
 DPF  Field
  Location: Nodal
  Unit: 
  1 entities 
  Data: 9 components and 1 elementary data 

  IDs                   data
  ------------          ----------
  0                     1.500000e+01   3.000000e+00   9.000000e+00   3.100000e+01   1.000000e+00   4.200000e+01   5.000000e+00   6.800000e+01   1.300000e+01   
                        

 

Data matrix Field:  
 [[15.  3.  9. 31.  1. 42.  5. 68. 13.]]

Append data to the Fields#

You can append a data array to a Field, this means adding a new entity with the new data in the Field. You have to give the Scoping id that this entities will have.

Append data to a scalar Field.

# Append the data
field_11.append(scopingid=6, data=data_9)

# Print the Field
print("Scalar Field : ", '\n',field_11, '\n')

# Print the Fields data
print("Data scalar Field: ", '\n',field_11.data, '\n')

Scalar Field :  
 DPF  Field
  Location: Nodal
  Unit: 
  7 entities 
  Data: 1 components and 7 elementary data 

  IDs                   data
  ------------          ----------
  0                     6.000000e+00   
                        
  1                     5.000000e+00   
                        
  2                     4.000000e+00   
                        
  ...

 

Data scalar Field:  
 [ 6.  5.  4.  3.  2.  1. 24.]

Append data to a vector Field.

# Append the data
field_21.append(scopingid=2, data=data_10)

# Print the Field
print("Vector Field : ", '\n',field_21, '\n')

# Print the Fields data
print("Data vector Field: ", '\n',field_21.data, '\n')

Vector Field :  
 DPF  Field
  Location: Nodal
  Unit: 
  3 entities 
  Data: 3 components and 3 elementary data 

  IDs                   data
  ------------          ----------
  0                     4.000000e+00   1.000000e+00   8.000000e+00   
                        
  1                     5.000000e+00   7.000000e+00   9.000000e+00   
                        
  2                     4.700000e+01   3.300000e+01   5.000000e+00   
                        

 

Data vector Field:  
 [[ 4.  1.  8.]
 [ 5.  7.  9.]
 [47. 33.  5.]]

Append data to a matrix Field.

# Append the data
field_51.append(scopingid=1, data=data_11)

# Print the Field
print("VMatrix Field : ", '\n',field_51, '\n')

# Print the Fields data
print("Data Matrix Field: ", '\n',field_51.data, '\n')

VMatrix Field :  
 DPF  Field
  Location: Nodal
  Unit: 
  2 entities 
  Data: 9 components and 2 elementary data 

  IDs                   data
  ------------          ----------
  0                     1.500000e+01   3.000000e+00   9.000000e+00   3.100000e+01   1.000000e+00   4.200000e+01   5.000000e+00   6.800000e+01   1.300000e+01   
                        
  1                     8.000000e+00   2.000000e+00   4.000000e+00   6.400000e+01   3.200000e+01   4.700000e+01   1.100000e+01   2.300000e+01   1.000000e+00   
                        

 

Data Matrix Field:  
 [[15.  3.  9. 31.  1. 42.  5. 68. 13.]
 [ 8.  2.  4. 64. 32. 47. 11. 23.  1.]]

Create a FieldsContainer#

A FieldsContainer is a collection of Field ordered by labels. Each Field of the FieldsContainer has an ID for each label. These ids allow splitting the fields on any criteria.

The most common FieldsContainer have the label ‘time’ with ids corresponding to time sets. The label ‘complex’, which is used in a harmonic analysis for example, allows real parts (id=0) to be separated from imaginary parts (id=1).

For more information on DPF data structures, see the DPF data structures tutorials section.

You can create a FieldsContainer by:

Create a FieldsContainer by an instance of this object#

After defining a FieldsContainer by an instance of this object you need to set the labels. Here, we define Fields over time steps labels. So, when you add a Field to the FieldsContainer you must specify the time step id it belongs to.

# Create the FieldsContainer object
fc_1 = dpf.FieldsContainer()

# Define the labels
fc_1.add_label(label="time")

# Add the Fields
fc_1.add_field(label_space={"time": 0}, field=field_21)
fc_1.add_field(label_space={"time": 1}, field=field_31)

# Print the FieldsContainer
print(fc_1)

DPF  Fields Container
  with 2 field(s)
  defined on labels: time 

  with:
  - field 0 {time:  0} with Nodal location, 3 components and 3 entities.
  - field 1 {time:  1} with Nodal location, 5 components and 2 entities.

Create a FieldsContainer with the fields_container_factory module#

The fields_container_factory module contains functions that create a FieldsContainer with predefined labels. Here, we use the over_time_freq_fields_container() function that create a FieldsContainer with a ‘time’ label.

# Create the FieldsContainer
fc_2 = dpf.fields_container_factory.over_time_freq_fields_container(fields=[field_21, field_31])

# Print the FieldsContainer
print(fc_2)

DPF  Fields Container
  with 2 field(s)
  defined on labels: time 

  with:
  - field 0 {time:  1} with Nodal location, 3 components and 3 entities.
  - field 1 {time:  2} with Nodal location, 5 components and 2 entities.