Create and manipulate a DPF Dataframe

In this script a Dataframe is generated by extracting a result from a static simulation. It then showcases different Dataframe viewing and manipulation possibilities.

Perform required imports

Perform required imports. # This example uses a supplied file that you can get by importing the DPF examples package.

from ansys.dpf import post
from ansys.dpf.post import examples

Get the Simulation object

Get the Simulation object that allows access to the result. The Simulation object must be instantiated with the path for the result file. For example, "C:/Users/user/my_result.rst" on Windows or "/home/user/my_result.rst" on Linux.

example_path = examples.download_crankshaft()
# to automatically detect the simulation type, use:
simulation = post.load_simulation(example_path)

# to enable auto-completion, use the equivalent:
simulation = post.StaticMechanicalSimulation(example_path)

# print the simulation to get an overview of what's available
print(simulation)

Static Mechanical Simulation.


Data Sources
------------------------------
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/ansys/dpf/core/examples/result_files/crankshaft/crankshaft.rst

DPF Model
------------------------------
Static analysis
Unit system: MKS: m, kg, N, s, V, A, degC
Physics Type: Mechanical
Available results:
     -  displacement: Nodal Displacement
     -  velocity: Nodal Velocity
     -  acceleration: Nodal Acceleration
     -  reaction_force: Nodal Force
     -  stress: ElementalNodal Stress
     -  elemental_volume: Elemental Volume
     -  stiffness_matrix_energy: Elemental Energy-stiffness matrix
     -  artificial_hourglass_energy: Elemental Hourglass Energy
     -  thermal_dissipation_energy: Elemental thermal dissipation energy
     -  kinetic_energy: Elemental Kinetic Energy
     -  co_energy: Elemental co-energy
     -  incremental_energy: Elemental incremental energy
     -  elastic_strain: ElementalNodal Strain
     -  element_euler_angles: ElementalNodal Element Euler Angles
     -  structural_temperature: ElementalNodal Structural temperature
------------------------------
DPF  Meshed Region:
  69762 nodes
  39315 elements
  Unit: m
  With solid (3D) elements
------------------------------
DPF  Time/Freq Support:
  Number of sets: 3
Cumulative     Time (s)       LoadStep       Substep
1              1.000000       1              1
2              2.000000       1              2
3              3.000000       1              3

Get a Dataframe object

Extract a result as a Dataframe

displacement_dataframe = simulation.displacement(all_sets=True)

# The Dataframe is displayed as a table, with row and column labels to identify the data.
print(displacement_dataframe)

            results       U (m)
            set_ids           1           2           3
node_ids components
    4872          X  5.6781e-06 -5.9469e-06 -3.4137e-05
                  Y  5.1667e-04  1.0318e-03  1.5417e-03
                  Z -3.2535e-06 -4.1346e-06 -2.6398e-06
    9005          X -2.6323e-06 -2.1432e-05 -5.5625e-05
                  Y  4.8445e-04  9.6717e-04  1.4448e-03
                  Z -4.9795e-07  1.2790e-06  5.3134e-06
     ...        ...         ...         ...         ...

Explore Index objects

The data labels are each defined by an Index object or one of its specialized subtypes.

# The Dataframe's column labels are defined in Dataframe.columns.
print(displacement_dataframe.columns)

MultiIndex<[ResultIndex<['U (m)']>, SetIndex<values=[1, 2, 3]>]>

A ResultIndex index defines the result stored in the Dataframe.

print(displacement_dataframe.columns[0])
# print(displacement_dataframe.columns.results_index)  # equivalent

ResultsIndex "results" with 1 values of <class 'str'> type

You can check values available for an Index

print(displacement_dataframe.columns[0].values)

['U (m)']

A SetIndex index defines the available set IDs available. A set ID is a unique identifier associated to each time-step, step and sub-step, or frequency available in a simulation. As shown next, an Index has a name and a list of values of a given type.

print(displacement_dataframe.columns[1])
print(displacement_dataframe.columns[1].values)

SetIndex "set_ids" with 3 values of <class 'int'> type
[1, 2, 3]

The Dataframe’s row labels are defined in Dataframe.index.

print(displacement_dataframe.index)

MultiIndex<[MeshIndex<name="node_ids", dtype=<class 'int'>>, CompIndex<name="components", dtype=<class 'str'>>]>

A MeshIndex defines the mesh entities for which data is available. It can store node IDs, element IDs, or face IDs.

print(displacement_dataframe.index[0])
# print(displacement_dataframe.index.mesh_index)  # equivalent

MeshIndex "node_ids" with uncounted values of <class 'int'> type

Since the list of possible values can be long and querying it can be costly, the list of available values may not be determined unless explicitly asked.

print(displacement_dataframe.index[0].values)

[ 4872  9005  9373 ... 34314 19123 19114]

The MeshIndex will then be updated to display the actual number of entities available.

print(displacement_dataframe.index[0])
# IMPORTANT: Note that the mesh entity IDs ordered based on the internal data storage structure,
# they are not by ascending order by default!

MeshIndex "node_ids" with 69762 values of <class 'int'> type

A CompIndex defines the result components for which data is available.

print(displacement_dataframe.index[1])
print(displacement_dataframe.index[1].values)

CompIndex "components" with 3 values of <class 'str'> type
['X', 'Y', 'Z']

Change the Dataframe print

Options exist to configure the way a Dataframe is displayed. You can change the number of data rows displayed with:

displacement_dataframe.display_max_rows = 9
print(displacement_dataframe)

            results       U (m)
            set_ids           1           2           3
node_ids components
    4872          X  5.6781e-06 -5.9469e-06 -3.4137e-05
                  Y  5.1667e-04  1.0318e-03  1.5417e-03
                  Z -3.2535e-06 -4.1346e-06 -2.6398e-06
    9005          X -2.6323e-06 -2.1432e-05 -5.5625e-05
                  Y  4.8445e-04  9.6717e-04  1.4448e-03
                  Z -4.9795e-07  1.2790e-06  5.3134e-06
    9373          X -2.6475e-05 -6.9632e-05 -1.2845e-04
                  Y  4.9502e-04  9.8751e-04  1.4741e-03
                  Z  6.9526e-06  1.6184e-05  2.7631e-05
     ...        ...         ...         ...         ...

Or the number of data columns displayed with:

displacement_dataframe.display_max_columns = 2
print(displacement_dataframe)
# Notice that the ``...`` symbols specify that the Dataframe is truncated in that direction.

            results       U (m)             ...
            set_ids           1           2 ...
node_ids components                         ...
    4872          X  5.6781e-06 -5.9469e-06 ...
                  Y  5.1667e-04  1.0318e-03 ...
                  Z -3.2535e-06 -4.1346e-06 ...
    9005          X -2.6323e-06 -2.1432e-05 ...
                  Y  4.8445e-04  9.6717e-04 ...
                  Z -4.9795e-07  1.2790e-06 ...
    9373          X -2.6475e-05 -6.9632e-05 ...
                  Y  4.9502e-04  9.8751e-04 ...
                  Z  6.9526e-06  1.6184e-05 ...
     ...        ...         ...         ... ...

The special case of ElementalNodal results

When dealing with results located on each node of each element (aka. ElementalNodal), an ElementNodeIndex index is added at creation to specify the node number in the element’s connectivity.

stress = simulation.stress()
print(stress)
print(stress.columns[2])

                results      S (Pa)
                set_ids           3
                   node           0           1           2           3
 element_ids components
       18357         XX  4.6183e+06 -1.5497e+06 -1.8339e+06 -5.5596e+06
                     YY  5.9405e+07  4.6302e+07  1.2961e+08 -1.2675e+07
                     ZZ  2.2825e+07  4.1579e+07  1.4117e+08 -1.4662e+08
                     XY -3.5088e+05  2.7884e+07  1.5350e+08  1.6123e+08
                     YZ -5.9452e+08 -4.9103e+08 -5.1941e+08 -4.5894e+08
                     XZ  7.6945e+06  2.3978e+07  3.2925e+07 -2.2203e+07
         ...        ...         ...         ...         ...         ...

ElementNodeIndex "node" with 1 values of <class 'int'> type

Data selection

To select specific columns or rows, use the index names as arguments for the DataFrame.select method, taking lists of values:

disp_X_1 = displacement_dataframe.select(
    set_ids=[1], node_ids=[4872, 9005], components=["X"]
)
print(disp_X_1)

            results       U (m)
            set_ids           1
node_ids components
    4872          X  5.6781e-06
    9005            -2.6323e-06

You can also select along an index using a zero-based position with Dataframe.iselect:

disp_Y_9005_3 = displacement_dataframe.iselect(
    set_ids=[2], node_ids=[1], components=[1]
)
print(disp_Y_9005_3)

            results      U (m)
            set_ids          3
node_ids components
    9005          Y 1.4448e-03

Extract data

Once the Dataframe contains the specific data you require, extract it as an array with:

print(disp_X_1.array)
# IMPORTANT: Note that for the extraction of the Dataframe's data as an array to make sense,
# you must first filter the columns label values to a unique combination of values.
# The exception is for ElementalNodal data, which is returned as a 2D array.
print(stress.array.shape)

[ 5.67807472e-06 -2.63230351e-06]
(157260, 6)

Plot a Dataframe

displacement_dataframe.plot()

Animate a transient Dataframe

displacement_dataframe.animate()