Extract and explore results metadata#

MAPDL LS-DYNA FLUENT CFX

This tutorial shows how to extract and explore results metadata from a result file.

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.

# 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)

Explore the results general metadata#

You can explore the general results metadata, before extracting the results, by using the ResultInfo object and its methods. This metadata includes:

  • Analysis type;

  • Physics type;

  • Number of results;

  • Unit system;

  • Solver version, date and time;

  • Job name;

# Define the ResultInfo object
result_info_1 = model_1.metadata.result_info

# Get the analysis type
analysis_type = result_info_1.analysis_type
# Print the analysis type
print("Analysis type: ",analysis_type, "\n")

# Get the physics type
physics_type = result_info_1.physics_type
# Print the physics type
print("Physics type: ",physics_type, "\n")

# Get the number of available results
number_of_results = result_info_1.n_results
# Print the number of available results
print("Number of available results: ",number_of_results, "\n")

# Get the unit system
unit_system = result_info_1.unit_system
# Print the unit system
print("Unit system: ",unit_system, "\n")

# Get the solver version, data and time
solver_version = result_info_1.solver_version
solver_date = result_info_1.solver_date
solver_time = result_info_1.solver_time

# Print the solver version, data and time
print("Solver version: ",solver_version, "\n")
print("Solver date: ", solver_date, "\n")
print("Solver time: ",solver_time, "\n")

# Get the job name
job_name = result_info_1.job_name
# Print the job name
print("Job name: ",job_name, "\n")

Analysis type:  static 

Physics type:  mechanical 

Number of available results:  21 

Unit system:  MKS: m, kg, N, s, V, A, degC 

Solver version:  17.0 

Solver date:  20150331 

Solver time:  93417 

Job name:  cp55

Explore a result metadata#

When you extract a result from a result file DPF stores it in a |Field|. Thus, this |Field| contains the metadata for the result associated with it. This metadata includes:

  • Location;

  • Scoping (type and quantity of entities);

  • Elementary data count (number of entities, how many data vectors we have);

  • Components count (vectors dimension, here we have a displacement so we expect to have 3 components (X, Y and Z));

  • Shape of the data stored (tuple with the elementary data count and the components count);

  • Fields size (length of the data entire vector (equal to the number of elementary data times the number of components));

  • Units of the data.

Here we will explore the metadata of the displacement results.

Start by extracting the displacement results.

# Extract the displacement results
disp_results = model_1.results.displacement.eval()

# Get the displacement field
disp_field = disp_results[0]

Explore the displacement results metadata:

# Get the location of the displacement data
location = disp_field.location
# Print the location
print("Location: ", location,'\n')

# Get the displacement Field scoping
scoping = disp_field.scoping
# Print the Field scoping
print("Scoping: ", '\n',scoping, '\n')

# Get the displacement Field scoping ids
scoping_ids = disp_field.scoping.ids  # Available entities ids
# Print the Field scoping ids
print("Scoping ids: ", scoping_ids, '\n')

# Get the displacement Field elementary data count
elementary_data_count = disp_field.elementary_data_count
# Print the elementary data count
print("Elementary data count: ", elementary_data_count, '\n')

# Get the displacement Field components count
components_count = disp_field.component_count
# Print the components count
print("Components count: ", components_count, '\n')

# Get the displacement Field size
field_size = disp_field.size
# Print the Field size
print("Size: ", field_size, '\n')

# Get the displacement Field shape
shape = disp_field.shape
# Print the Field shape
print("Shape: ", shape, '\n')

# Get the displacement Field unit
unit = disp_field.unit
# Print the displacement Field unit
print("Unit: ", unit, '\n')

Location:  Nodal 

Scoping:  
 DPF  Scoping: 
  with Nodal location and 3820 entities
 

Scoping ids:  [ 525  534  533 ... 3469 3817 3825] 

Elementary data count:  3820 

Components count:  3 

Size:  11460 

Shape:  (3820, 3) 

Unit:  m