Note
Go to the end to download the full example code.
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/3.10.14/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()
Total running time of the script: (0 minutes 2.151 seconds)