# Copyright (C) 2020 - 2026 ANSYS, Inc. and/or its affiliates. # SPDX-License-Identifier: MIT # # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # _order: 1 """ .. _ref_tutorials_load_custom_data: Load custom data in DPF ======================= This tutorial shows how to represent your custom data in DPF data storage structures. To import your custom data in DPF, you must create a DPF data structure to store it. DPF uses :class:`Field` and :class:`FieldsContainer` objects to handle data. The ``Field`` is a homogeneous array and a ``FieldsContainer`` is a labeled collection of ``Field`` objects. For more information on DPF data structures, see the :ref:`ref_tutorials_data_structures` tutorials section. """ ############################################################################### # Define the data # --------------- # # Create Python lists with the data to be stored in 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 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] ############################################################################### # Import the PyDPF-Core library # ----------------------------- # # Import the ``ansys.dpf.core`` module. from ansys.dpf import core as dpf ############################################################################### # Define the Fields sizing # ------------------------ # # A :class:`Field` must always be given: # # - A :class:`location` and a # :class:`Scoping`. # - A :class:`nature` and a # :class:`dimensionality` # (number of data components for each entity). # # Define the number of entities for scalar, vector, and matrix Fields. # Scalar Fields: 6 entities with 1 component each num_entities_1 = 6 # Vector Fields: 2 entities with 3 or 5 components each num_entities_2 = 2 # Matrix Fields: 1 entity num_entities_3 = 1 ############################################################################### # Create scalar Fields # -------------------- # # **Create a scalar Field by instantiating the** ``Field`` **object** # # The default ``nature`` of the ``Field`` object is ``'vector'``. Use the # ``nature`` argument or the # :func:`Field.dimensionality` method # to create a scalar Field. # Instantiate the Field with a scalar nature 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") ############################################################################### # Instantiate the Field and use the ``dimensionality`` method to set scalar nature. field_12 = dpf.Field(nentities=num_entities_1) field_12.dimensionality = dpf.Dimensionality([1]) field_12.scoping.ids = range(num_entities_1) print("Scalar Field: ", "\n", field_12, "\n") ############################################################################### # **Create a scalar Field using the** ``fields_factory`` **module** # # Use the :func:`create_scalar_field()` # function. Its default ``nature`` is ``'scalar'`` with a ``'1D'`` dimensionality. field_13 = dpf.fields_factory.create_scalar_field(num_entities=num_entities_1) field_13.scoping.ids = range(num_entities_1) print("Scalar Field: ", "\n", field_13, "\n") ############################################################################### # Use the :func:`field_from_array()` # function, which takes the data directly as input. field_14 = dpf.fields_factory.field_from_array(arr=data_1) field_14.scoping.ids = range(num_entities_1) print("Scalar Field from array: ", "\n", field_14, "\n") ############################################################################### # Create vector Fields # -------------------- # # **Create vector Fields by instantiating the** ``Field`` **object** # # The default ``nature`` is ``'vector'`` and the default dimensionality is ``'3D'``. # Use the :func:`Field.dimensionality` # method to set a different dimensionality (here ``'5D'``). # 3D vector Field (default dimensionality) field_21 = dpf.Field(nentities=num_entities_2) field_21.scoping.ids = range(num_entities_2) print("3D vector Field: ", "\n", field_21, "\n") # 5D vector Field field_31 = dpf.Field(nentities=num_entities_2) field_31.dimensionality = dpf.Dimensionality([5]) field_31.scoping.ids = range(num_entities_2) print("5D vector Field: ", "\n", field_31, "\n") ############################################################################### # **Create vector Fields using the** ``fields_factory`` **module** # # Use the :func:`create_vector_field()` # function with the ``num_comp`` argument to set the dimensionality. field_22 = dpf.fields_factory.create_vector_field(num_entities=num_entities_2, num_comp=3) field_22.scoping.ids = range(num_entities_2) print("3D vector Field: ", "\n", field_22, "\n") field_32 = dpf.fields_factory.create_vector_field(num_entities=num_entities_2, num_comp=5) field_32.scoping.ids = range(num_entities_2) print("5D vector Field: ", "\n", field_32, "\n") ############################################################################### # Use the # :func:`create_3d_vector_field()` # function to create a 3D vector Field directly. field_25 = dpf.fields_factory.create_3d_vector_field(num_entities=num_entities_2) field_25.scoping.ids = range(num_entities_2) print("3D vector Field: ", "\n", field_25, "\n") ############################################################################### # Create matrix Fields # -------------------- # # Use the # :func:`create_matrix_field()` # function from the ``fields_factory`` module. Use the ``num_lines`` and ``num_col`` # arguments to define the matrix dimensionality. # (2, 2) matrix Field field_41 = dpf.fields_factory.create_matrix_field( num_entities=num_entities_3, num_lines=2, num_col=2 ) field_41.scoping.ids = range(num_entities_3) print("Matrix Field (2,2): ", "\n", field_41, "\n") # (3, 3) matrix Fields field_51 = dpf.fields_factory.create_matrix_field( num_entities=num_entities_3, num_lines=3, num_col=3 ) field_51.scoping.ids = range(num_entities_3) field_52 = dpf.fields_factory.create_matrix_field( num_entities=num_entities_3, num_lines=3, num_col=3 ) field_52.scoping.ids = range(num_entities_3) print("Matrix Field 1 (3,3): ", "\n", field_51, "\n") print("Matrix Field 2 (3,3): ", "\n", field_52, "\n") ############################################################################### # Set data to the Fields # ---------------------- # # Use the :attr:`Field.data` attribute # to set a data array to a ``Field``. The data can be a 1D or 2D Numpy array or # Python list. # Set data to the scalar Field field_11.data = data_1 print("Scalar Field: ", "\n", field_11, "\n") print("Data scalar Field: ", "\n", field_11.data, "\n") ############################################################################### # Set data to the vector Fields. field_21.data = data_3 print("3D vector Field: ", "\n", field_21, "\n") field_31.data = data_4 print("5D vector Field: ", "\n", field_31, "\n") # Set 2D data to a 3D vector Field field_22.data = data_5 print("3D vector Field (from 2D list): ", "\n", field_22, "\n") ############################################################################### # Set data to the matrix Fields. field_41.data = data_6 print("Matrix Field (2,2): ", "\n", field_41.data, "\n") field_51.data = data_7 print("Matrix Field 1 (3,3): ", "\n", field_51.data, "\n") field_52.data = data_8 print("Matrix Field 2 (3,3): ", "\n", field_52.data, "\n") ############################################################################### # Append data to the Fields # ------------------------- # # Use the :func:`append()` method to add a new # entity with data to the Field. You must provide the ``scopingid`` that this entity # will have. # Append data to the scalar Field field_11.append(scopingid=6, data=data_9) print("Scalar Field after append: ", "\n", field_11, "\n") print("Data scalar Field: ", "\n", field_11.data, "\n") ############################################################################### # Append data to a vector Field. field_21.append(scopingid=2, data=data_10) print("Vector Field after append: ", "\n", field_21, "\n") ############################################################################### # Append data to a matrix Field. field_51.append(scopingid=1, data=data_11) print("Matrix Field after append: ", "\n", field_51, "\n") ############################################################################### # Create a ``FieldsContainer`` # ---------------------------- # # A :class:`FieldsContainer` is a # collection of ``Field`` objects ordered by labels. Each ``Field`` in the # ``FieldsContainer`` has an ID for each label. # # The most common ``FieldsContainer`` use the label ``'time'`` with IDs corresponding # to time sets. # # **Create a** ``FieldsContainer`` **by instantiating the object** # # After instantiation, set the labels and then add fields with their label space. fc_1 = dpf.FieldsContainer() fc_1.add_label(label="time") fc_1.add_field(label_space={"time": 0}, field=field_21) fc_1.add_field(label_space={"time": 1}, field=field_31) print(fc_1) ############################################################################### # **Create a** ``FieldsContainer`` **using the** ``fields_container_factory`` **module** # # Use the # :func:`over_time_freq_fields_container()` # function to create a ``FieldsContainer`` with a predefined ``'time'`` label. fc_2 = dpf.fields_container_factory.over_time_freq_fields_container(fields=[field_21, field_31]) print(fc_2)