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.