DPF Python gRPC client
Project description
DPF - Ansys Data Processing Framework
The Data Processing Framework (DPF) is designed to provide numerical simulation users/engineers with a toolbox for accessing and transforming simulation data. DPF can access data from solver result files as well as several neutral formats (csv, hdf5, vtk, etc.). Various operators are available allowing the manipulation and the transformation of this data.
DPF is a workflow-based framework which allows simple and/or complex evaluations by chaining operators. The data in DPF is defined based on physics agnostic mathematical quantities described in a self-sufficient entity called field. This allows DPF to be a modular and easy to use tool with a large range of capabilities. It's a product designed to handle large amount of data.
The Python ansys.dpf.core module provides a Python interface to
the powerful DPF framework enabling rapid post-processing of a variety
of Ansys file formats and physics solutions without ever leaving a
Python environment.
Installation
Install this repository with:
pip install ansys-dpf-core
You can also clone and install this repository with:
git clone https://github.com/pyansys/DPF-Core
cd DPF-Core
pip install . --user
Running DPF
Brief Demo
Provided you have ANSYS 2021R1 installed, a DPF server will start automatically once you start using DPF.
Opening a result file generated from Ansys workbench or MAPDL is as easy as:
>>> from ansys.dpf.core import Model
>>> model = Model('file.rst')
>>> print(model)
DPF Model
------------------------------
Static analysis
Unit system: Metric (m, kg, N, s, V, A)
Physics Type: Mecanic
Available results:
- displacement
- element_nodal_forces
- volume
- energy_stiffness_matrix
- hourglass_energy
- thermal_dissipation_energy
- kinetic_energy
- co_energy
- incremental_energy
- temperature
Open up an result with:
>>> model.displacement
Then start linking operators with:
>>> norm = core.Operator('norm_fc')
Starting the Service
The ansys.dpf.core automatically starts the DPF service in the
background and connects to it. If you need to connect to an existing
remote DPF instance, use the connect_to_server function:
from ansys.dpf import core
connect_to_server('10.0.0.22, 50054)
Once connected, this connection will remain for the duration of the module until you exit python or connect to a different server.
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