Extract and explore results data#
MAPDL LS-DYNA FLUENT CFX
This tutorial shows how to extract and explore results data from a result file.
When you extract a result from a result file DPF stores it in a |Field|. Thus, this |Field| contains the data of the result associated with it.
Note
When DPF-Core returns the |Field| object, what Python actually has is a client-side representation of the |Field|, not the entirety of the |Field| itself. This means that all the data of the field is stored within the DPF service. This is important because when building your workflows, the most efficient way of interacting with result data is to minimize the exchange of data between Python and DPF, either by using operators or by accessing exclusively the data that is needed.
Download tutorial as Python script
Download tutorial as Jupyter notebook
Get the result file#
First, import a result file. For this tutorial, you can use one available in the |Examples| module. For more information about how to import your own result file in DPF, see the Import a result file in DPF tutorial.
Here, we extract the displacement results. The displacement |Result| object gives a |FieldsContainer| when evaluated. Thus, we get a |Field| from this |FieldsContainer|.
# Import the ``ansys.dpf.core`` module
from ansys.dpf import core as dpf
# Import the examples module
from ansys.dpf.core import examples
# Import the operators module
from ansys.dpf.core import operators as ops
# Define the result file path
result_file_path_1 = examples.download_transient_result()
# Create the model
model_1 = dpf.Model(data_sources=result_file_path_1)
# Extract the displacement results for the last time step
disp_results = model_1.results.displacement.on_last_time_freq.eval()
# Get the displacement field for the last time step
disp_field = disp_results[0]
# Print the displacement Field
print(disp_field)
DPF displacement_0.676628s Field
Location: Nodal
Unit: m
3820 entities
Data: 3 components and 3820 elementary data
Nodal
IDs data(m)
------------ ----------
525 6.255867e-03 -1.392431e-02 2.426972e-05
534 1.796759e-02 -2.748128e-02 1.838220e-05
533 -6.726646e-03 -3.213735e-02 1.671591e-04
...
Extract all the data from a |Field|#
You can extract the entire data in a |Field| as:
An array (numpy array);
A list.
Data as an array#
# Get the displacement data as an array
data_array = disp_field.data
# Print the data as an array
print("Displacement data as an array: ", '\n', data_array)
Displacement data as an array:
[[ 6.25586668e-03 -1.39243136e-02 2.42697211e-05]
[ 1.79675948e-02 -2.74812825e-02 1.83822050e-05]
[-6.72664571e-03 -3.21373459e-02 1.67159110e-04]
...
[-6.07730368e-03 3.22569017e-02 3.10184480e-04]
[-3.51074714e-06 2.16872928e-08 6.40738989e-05]
[ 1.03542516e-02 -3.53018374e-03 -3.98914380e-05]]
Note that this array is a genuine, local, numpy array (overloaded by the DPFArray):
# Print the array type
print("Array type: ", type(data_array))
Array type: <class 'ansys.dpf.gate.dpf_array.DPFArray'>
Data as a list#
# Get the displacement data as a list
data_list = disp_field.data_as_list
# Print the data as a list
print("Displacement data as a list: ", '\n', data_list)
Displacement data as a list:
[0.00625586667570039, -0.013924313639096205, 2.426972107510151e-05, 0.01796759477724075, -0.027481282478258835, 1.8382204983237606e-05, -0.006726645712597537, -0.03213734585648497, 0.00016715911014525897, -0.005360856984291837, -0.019866555283474557, 0.00010964905930014374, 0.012111730726928581, -0.020702798058378057, 2.132596302553595e-05, 0.0060835578001471385, -0.032265369150917884, 0.00010041423614069224, -0.006043751348444691, -0.02600195056997976, 0.00013840408472270136, 0.0004475048443302506, -0.01689543446218377, 6.695939019852346e-05, 0.014059136558968536, -0.002458750766270521, -6.374214364182882e-05, 0.03213666193507017, -0.006727056543742505, -0.00014116274654624557, 0.023097899243126375, -0.004592903649052234, -0.00010245244509801999, 0.027116228331735312, -0.018513419675270974, -6.644835024209939e-05, 0.010157501618972675, -0.008191532206188977, -1.973621127758545e-05, -0.027480508371300262, -0.017967787881222386, 0.00021801647560933248, -0.015654649062875888, 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Extract specific data from a field#
If you need to access data for specific entities (node, element …), you can extract it with two approaches:
Based on its index (data position on the |Field|) by using the
get_entity_data()method;Based on the entities id by using the
get_entity_data_by_id()method.
The |Field| data is organized with respect to its scoping ids. Note that the element with id=533 would correspond to an index=2 within the |Field|.
# Get the index of the entity with id=533
index_533_entity = disp_field.scoping.index(id=533)
# Print the index
print("Index entity id=533: ",index_533_entity)
Index entity id=533: 2
Be aware that scoping IDs are not sequential. You would get the id of the element in the 533 position of the |Field| with:
# Get the id of the entity with index=533
id_533_entity = disp_field.scoping.id(index=533)
print("Id entity index=533: ",id_533_entity)
Id entity index=533: 3635
Get the data by the entity index#
# Get the data from the third entity in the field
data_3_entity = disp_field.get_entity_data(index=3)
# Print the data
print("Data entity index=3: ", data_3_entity)
Data entity index=3: [[-0.00536086 -0.01986656 0.00010965]]
Get the data by the entity id#
# Get the data from the entity with id=533
data_533_entity = disp_field.get_entity_data_by_id(id=533)
# Print the data
print("Data entity id=533: ", data_533_entity)
Data entity id=533: [[-0.00672665 -0.03213735 0.00016716]]
Extract specific data from a field using a loop over the array#
While the methods above are acceptable when requesting data for a few elements or nodes, they should not be used when looping over the entire array. For efficiency, a |Field| data can be recovered locally before sending a large number of requests:
# Create a deep copy of the field that can be accessed and modified locally.
with disp_field.as_local_field() as f:
for i in disp_field.scoping.ids[2:50]:
f.get_entity_data_by_id(i)
# Print the field
print(f)
DPF displacement_0.676628s Field
Location: Nodal
Unit: m
3820 entities
Data: 3 components and 3820 elementary data
Nodal
IDs data(m)
------------ ----------
525 6.255867e-03 -1.392431e-02 2.426972e-05
534 1.796759e-02 -2.748128e-02 1.838220e-05
533 -6.726646e-03 -3.213735e-02 1.671591e-04
...