| Name | pyLump JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | |
| Summary | Multi Degree of Freedom (mass-spring-damper) Models. |
| upload_time | 2023-11-15 09:12:46 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.10 |
| license | |
| keywords |
mdof
dynamics
lumped mass
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
pyLump
======
Multi Degree of Freedom (mass-spring-damper) Models.
----------------------------------------------------
For more information check out the showcase examples and see documentation_.
Basic ``pyLump`` usage:
--------------------------
Make an instance of the ``Model`` class:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: python
a = pyLump.Model(
n_dof,
mass,
stiffness,
damping,
boundaries="both"
)
Getting system properties:
~~~~~~~~~~~~~~~~~~~~~~~~~~
There are several methods available for different system properties:
.. code:: python
M = a.get_mass_matrix()
K = a.get_stiffness_matrix()
C = a.get_damping_matrix()
eig_freq = a.get_eig_freq()
eig_val = a.get_eig_val()
eig_vec = a.get_eig_vec()
d_ratios = a.get_damping_ratios()
Obtaining frequency response functions and impulse response functions:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
To obtain the FRF (frequency response function) matrix and the IRF (impulse reponse function) matrix we use the following methods:
.. code:: python
FRF_matrix = a.get_FRF_matrix(freq)
IRF_matrix = a.get_IRF_matrix(freq)
Calculating response:
~~~~~~~~~~~~~~~~~~~~~
We can calculate the systems response based on known excitation the following way:
.. code:: python
response = a.get_response(
exc_dof,
exc,
sampling_rate,
resp_dof
)
.. _documentation: https://pyLump.readthedocs.io/en/latest/
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