Skip to main content

Pythonic interface to ANSYS binary files

Project description

https://img.shields.io/pypi/v/pyansys.svg https://dev.azure.com/femorph/pyansys/_apis/build/status/akaszynski.pyansys?branchName=master
This Python module allows you to:
  • Interactively control an instance of ANSYS v14.5 + using Python on Linux, >=17.0 on Windows.

  • Extract data directly from binary ANSYS v14.5+ files and to display or animate them.

  • Rapidly read in binary result (.rst), binary mass and stiffness (.full), and ASCII block archive (.cdb) files.

See the Documentation page for more details.

Installation

Installation through pip:

pip install pyansys

You can also visit GitHub to download the source.

Quick Examples

Many of the following examples are built in and can be run from the build-in examples module. For a quick demo, run:

from pyansys import examples
examples.run_all()

Controlling ANSYS

Create an instance of ANSYS and interactively send commands to it. This is a direct interface and does not rely on writing a temporary script file. You can also generate plots using matplotlib.

import os
import pyansys

path = os.getcwd()
ansys = pyansys.Mapdl(run_location=path, interactive_plotting=True)

# create a square area using keypoints
ansys.prep7()
ansys.k(1, 0, 0, 0)
ansys.k(2, 1, 0, 0)
ansys.k(3, 1, 1, 0)
ansys.k(4, 0, 1, 0)
ansys.l(1, 2)
ansys.l(2, 3)
ansys.l(3, 4)
ansys.l(4, 1)
ansys.al(1, 2, 3, 4)
ansys.aplot()
ansys.save()
ansys.exit()
https://github.com/akaszynski/pyansys/raw/master/docs/images/aplot.png

Loading and Plotting an ANSYS Archive File

ANSYS archive files containing solid elements (both legacy and current), can be loaded using Archive and then converted to a vtk object.

import pyansys
from pyansys import examples

# Sample *.cdb
filename = examples.hexarchivefile

# Read ansys archive file
archive = pyansys.Archive(filename)

# Print raw data from cdb
for key in archive.raw:
   print("%s : %s" % (key, archive.raw[key]))

# Create a vtk unstructured grid from the raw data and plot it
grid = archive.parse_vtk(force_linear=True)
grid.plot(color='w', show_edges=True)

# write this as a vtk xml file
grid.save('hex.vtu')

# or as a vtk binary
grid.save('hex.vtk')
https://github.com/akaszynski/pyansys/raw/master/docs/images/hexbeam.png

You can then load this vtk file using pyvista or another program that uses VTK.

# Load this from vtk
import pyvista as pv
grid = pv.UnstructuredGrid('hex.vtu')
grid.plot()

Loading the Result File

This example reads in binary results from a modal analysis of a beam from ANSYS.

# Load the reader from pyansys
import pyansys
from pyansys import examples

# Sample result file
rstfile = examples.rstfile

# Create result object by loading the result file
result = pyansys.read_binary(rstfile)

# Beam natural frequencies
freqs = result.time_values
>>> print(freq)
[ 7366.49503969  7366.49503969 11504.89523664 17285.70459456
  17285.70459457 20137.19299035]

# Get the 1st bending mode shape.  Results are ordered based on the sorted
# node numbering.  Note that results are zero indexed
nnum, disp = result.nodal_solution(0)
>>> print(disp)
[[ 2.89623914e+01 -2.82480489e+01 -3.09226692e-01]
 [ 2.89489249e+01 -2.82342416e+01  2.47536161e+01]
 [ 2.89177130e+01 -2.82745126e+01  6.05151053e+00]
 [ 2.88715048e+01 -2.82764960e+01  1.22913304e+01]
 [ 2.89221536e+01 -2.82479511e+01  1.84965333e+01]
 [ 2.89623914e+01 -2.82480489e+01  3.09226692e-01]
 ...

Plotting Nodal Results

As the geometry of the model is contained within the result file, you can plot the result without having to load any additional geometry. Below, displacement for the first mode of the modal analysis beam is plotted using VTK.

# Plot the displacement of Mode 0 in the x direction
result.plot_nodal_solution(0, 'x', label='Displacement')
https://github.com/akaszynski/pyansys/raw/master/docs/images/hexbeam_disp.png

Results can be plotted non-interactively and screenshots saved by setting up the camera and saving the result. This can help with the visualization and post-processing of a batch result.

First, get the camera position from an interactive plot:

>>> cpos = result.plot_nodal_solution(0)
>>> print(cpos)
[(5.2722879880979345, 4.308737919176047, 10.467694436036483),
 (0.5, 0.5, 2.5),
 (-0.2565529433509593, 0.9227952809887077, -0.28745339908049733)]

Then generate the plot:

result.plot_nodal_solution(0, 'x', label='Displacement', cpos=cpos,
                           screenshot='hexbeam_disp.png',
                           window_size=[800, 600], interactive=False)

Stress can be plotted as well using the below code. The nodal stress is computed in the same manner that ANSYS uses by to determine the stress at each node by averaging the stress evaluated at that node for all attached elements. For now, only component stresses can be displayed.

# Display node averaged stress in x direction for result 6
result.plot_nodal_stress(5, 'Sx')
https://github.com/akaszynski/pyansys/raw/master/docs/images/beam_stress.png

Nodal stress can also be generated non-interactively with:

result.plot_nodal_stress(5, 'Sx', cpos=cpos, screenshot=beam_stress.png,
                       window_size=[800, 600], interactive=False)

Animating a Modal Solution

Mode shapes from a modal analysis can be animated using animate_nodal_solution:

result.animate_nodal_solution(0)

If you wish to save the animation to a file, specify the movie_filename and animate it with:

result.animate_nodal_solution(0, movie_filename='/tmp/movie.mp4', cpos=cpos)
https://github.com/akaszynski/pyansys/raw/master/docs/images/beam_mode_shape.gif

Reading a Full File

This example reads in the mass and stiffness matrices associated with the above example.

# Load the reader from pyansys
import pyansys
from scipy import sparse

# load the full file
fobj = pyansys.FullReader('file.full')
dofref, k, m = fobj.load_km()  # returns upper triangle only

# make k, m full, symmetric matrices
k += sparse.triu(k, 1).T
m += sparse.triu(m, 1).T

If you have scipy installed, you can solve the eigensystem for its natural frequencies and mode shapes.

from scipy.sparse import linalg

# condition the k matrix
# to avoid getting the "Factor is exactly singular" error
k += sparse.diags(np.random.random(k.shape[0])/1E20, shape=k.shape)

# Solve
w, v = linalg.eigsh(k, k=20, M=m, sigma=10000)

# System natural frequencies
f = np.real(w)**0.5/(2*np.pi)

print('First four natural frequencies')
for i in range(4):
    print '{:.3f} Hz'.format(f[i])
First four natural frequencies
1283.200 Hz
1283.200 Hz
5781.975 Hz
6919.399 Hz

Additional Tools

There are additional tools created by @natter1 at pyansysTools which include the following features:

  • Inline class: Implementing the ANSYS inline functions

  • Macros class: Macros for repeating tasks

  • The geo2d class: Easily create 2d geometries

You can also install pyansystools with

` pip install pyansystools `

License and Acknowledgments

pyansys is licensed under the MIT license.

This module, pyansys makes no commercial claim over ANSYS whatsoever. This tool extends the functionality of ANSYS by adding a Python interface in both file interface as well as interactive scripting without changing the core behavior or license of the original software. The use of the interactive APDL control of pyansys requires a legally licensed local copy of ANSYS.

To get a copy of ANSYS, please visit ANSYS

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

pyansys-0.40.2-cp38-cp38-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.8 Windows x86-64

pyansys-0.40.2-cp38-cp38-manylinux1_x86_64.whl (5.1 MB view details)

Uploaded CPython 3.8

pyansys-0.40.2-cp38-cp38-macosx_10_9_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

pyansys-0.40.2-cp37-cp37m-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.7m Windows x86-64

pyansys-0.40.2-cp37-cp37m-manylinux1_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.7m

pyansys-0.40.2-cp37-cp37m-macosx_10_9_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

pyansys-0.40.2-cp36-cp36m-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.6m Windows x86-64

pyansys-0.40.2-cp36-cp36m-manylinux1_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.6m

pyansys-0.40.2-cp36-cp36m-macosx_10_9_x86_64.whl (2.1 MB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

pyansys-0.40.2-cp35-cp35m-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.5m Windows x86-64

pyansys-0.40.2-cp35-cp35m-manylinux1_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.5m

File details

Details for the file pyansys-0.40.2-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pyansys-0.40.2-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 2.0 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.8.3

File hashes

Hashes for pyansys-0.40.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 5f67964b56df6aa26f29d85c5b9c07a8f2bcbf18012e6f15f1e2c8e86ad03dda
MD5 f75a1169d100e3e0c744556330c0d774
BLAKE2b-256 3b2e4aa607c91b6a8231ef69f1908abefd6fd21261045941d7f5a7ea93537530

See more details on using hashes here.

File details

Details for the file pyansys-0.40.2-cp38-cp38-manylinux1_x86_64.whl.

File metadata

  • Download URL: pyansys-0.40.2-cp38-cp38-manylinux1_x86_64.whl
  • Upload date:
  • Size: 5.1 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.8.3

File hashes

Hashes for pyansys-0.40.2-cp38-cp38-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 5cab50add323ac835f952f2e4692fc82fa457fc44903c98ec601fb2b04f2518f
MD5 c2ad43a863f7372e74d5d9f343593041
BLAKE2b-256 45d5a880ad750b5dff4433acb19969034b77bac6e90da95868b1d2cc4592438d

See more details on using hashes here.

File details

Details for the file pyansys-0.40.2-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pyansys-0.40.2-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 2.1 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.8.3

File hashes

Hashes for pyansys-0.40.2-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 42d7e918f2c578affa5a56ef68200a55874bc6883595b8ad0fecd7dd7a29af06
MD5 2b1f7527569a906cc8c4bbceee951df1
BLAKE2b-256 6fd035e2fd5a0aa1a67acc3e6ad7305c51c2c86f4f7231a0846f646be40907ec

See more details on using hashes here.

File details

Details for the file pyansys-0.40.2-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: pyansys-0.40.2-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 2.0 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.7.7

File hashes

Hashes for pyansys-0.40.2-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 a0e53b1b4edd7226b7377e7c5c3702ddbb8c1756f68fd4896e512ca3aa18745f
MD5 779e0dec136082ad51c8b2103dc66061
BLAKE2b-256 1bfed33aa398d11b076cc01e4fd73e6bd99e33a11963cdc977938b3865831ed5

See more details on using hashes here.

File details

Details for the file pyansys-0.40.2-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: pyansys-0.40.2-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.7.7

File hashes

Hashes for pyansys-0.40.2-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 b26990fca7d67fbdc15a4a0b6e4d87f620d4f22124f3dab7d88a21e86a4e12c2
MD5 a9d3253ecb24aaf3a6c9ed3ddd33d2da
BLAKE2b-256 f39012f48fadb381099f4f878f81ef858e38347ca76e596038a3768c3beff8ff

See more details on using hashes here.

File details

Details for the file pyansys-0.40.2-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pyansys-0.40.2-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 2.1 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.7.7

File hashes

Hashes for pyansys-0.40.2-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 7c4bebf6cadfb63f331e202b42b3ad0bb1cc3e8ac8b5156b2f19ed1ac6b55455
MD5 66d234c350daecc2a18e16d42c4f8d80
BLAKE2b-256 7ec64fcbd7e8fd48fe80823b44ea430eb8561b6aa021c7797dfe78eba6943244

See more details on using hashes here.

File details

Details for the file pyansys-0.40.2-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: pyansys-0.40.2-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 2.0 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.6.8

File hashes

Hashes for pyansys-0.40.2-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 8ac9e71f202515fc9ec06fbbfe96f87777aa0b7ac785486b8d8c0e0849f9e3af
MD5 8a242ff2b4e6d5a3e5bfc32f323c85ba
BLAKE2b-256 9f207c434f61d07b6de3df6049e8f9575745a3e7a8e77a633c4abbddff765911

See more details on using hashes here.

File details

Details for the file pyansys-0.40.2-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: pyansys-0.40.2-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.6.10

File hashes

Hashes for pyansys-0.40.2-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 731e5573f7a0bc15813d485514b58d228a8e548de0edbab386761f38241e4f5d
MD5 13f0d2b5267b931afe6c5392353e8729
BLAKE2b-256 e8b6fce8dbacf67f953492c49a2fdca111885fb26de0125e7c37e896832d84da

See more details on using hashes here.

File details

Details for the file pyansys-0.40.2-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pyansys-0.40.2-cp36-cp36m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 2.1 MB
  • Tags: CPython 3.6m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.6.8

File hashes

Hashes for pyansys-0.40.2-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 5894e6a3915bb70a7bd93bb1d129561d1349e9e152ac48dc098d4ca3b250becb
MD5 07efa82c552463606bb83d1df44fba11
BLAKE2b-256 63b623a56363084536b7480d179b49365df1700347679d1fb56e185ff7d69954

See more details on using hashes here.

File details

Details for the file pyansys-0.40.2-cp35-cp35m-win_amd64.whl.

File metadata

  • Download URL: pyansys-0.40.2-cp35-cp35m-win_amd64.whl
  • Upload date:
  • Size: 2.0 MB
  • Tags: CPython 3.5m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.15.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.5.4

File hashes

Hashes for pyansys-0.40.2-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 d8e88a9e31312cc2986c9f9bcf420e65576fe7f8caac8b18db0614622e1b74fb
MD5 99caea701f6f3e8c0a6f98db439dbd8f
BLAKE2b-256 d7a185cff6bc12104b138b2b1cd9b8698b478ae69776c4832286b7353fc4c1c1

See more details on using hashes here.

File details

Details for the file pyansys-0.40.2-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

  • Download URL: pyansys-0.40.2-cp35-cp35m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.15.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/28.8.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.5.9

File hashes

Hashes for pyansys-0.40.2-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 82d2cf47ac54f7cccd454f395232ec4d42da8519b357bc05fa415256af4b88b2
MD5 fbbcd4121f49e86f0c6ca26c825c839c
BLAKE2b-256 a4a3cf11fa2a61b8825e9a97e32fae51843158d2dfcf8d7a519ab9631d71a990

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page