o3seespy

Object-oriented native python version of OpenSees

Features

This package provides:

1. A 'pythonic' version of OpenSees - all input parameters to create OpenSees objects are all lowercase key-value arguments - all class objects are CamelCase - static string variables defined in ALL_CAPS - Where possible the exact name used in the original TCL version has been kept
2. Fully namespaced package allowing full auto-complete e.g. 'o3.uniaxial_material.Steel01(...)'
3. Replication of underlying object-oriented C++ source code using Python objects.
4. Type checking of inputs before calling C++ OpenSees code, so that python debugging and errors can be viewed
5. In code documentation using python docstrings - can view the documentation within your IDE
6. Additional features for using OpenSees in python: - saving and loading data directly from OpenSees into numpy arrays - saving and loading data directly from OpenSees into json files - Save an entire model as a json file - allows efficient passing of models between servers
7. All object numbering handled by objects - no need for number tags!
8. Additional logic checking of optional inputs

How to Use

Installation

pip install o3seespy

Example: Inelastic SDOF

import numpy as np
import matplotlib.pyplot as plt

import o3seespy as o3

from tests.conftest import TEST_DATA_DIR

# Load a ground motion
dt = 0.01
rec = np.loadtxt(TEST_DATA_DIR + 'test_motion_dt0p01.txt')

# Define inelastic SDOF
period = 1.0
xi = 0.05
mass = 1.0
f_yield = 1.5  # Reduce this to make it nonlinear
r_post = 0.0

# Initialise OpenSees instance
osi = o3.OpenSeesInstance(ndm=2, state=0)

# Establish nodes
bot_node = o3.node.Node(osi, 0, 0)
top_node = o3.node.Node(osi, 0, 0)

# Fix bottom node
o3.Fix3DOF(osi, top_node, o3.cc.FREE, o3.cc.FIXED, o3.cc.FIXED)
o3.Fix3DOF(osi, bot_node, o3.cc.FIXED, o3.cc.FIXED, o3.cc.FIXED)
# Set out-of-plane DOFs to be slaved
o3.EqualDOF(osi, top_node, bot_node, [o3.cc.Y, o3.cc.ROTZ])

# nodal mass (weight / g):
o3.Mass(osi, top_node, mass, 0., 0.)

# Define material
k_spring = 4 * np.pi ** 2 * mass / period ** 2
bilinear_mat = o3.uniaxial_material.Steel01(osi, fy=f_yield, e0=k_spring, b=r_post)

# Assign zero length element, # Note: pass actual node and material objects into element
o3.element.ZeroLength(osi, [bot_node, top_node], mats=[bilinear_mat], dirs=[o3.cc.DOF2D_X], r_flag=1)

# Define the dynamic analysis
load_tag_dynamic = 1
pattern_tag_dynamic = 1

# Define the dynamic analysis
acc_series = o3.time_series.Path(osi, dt=dt, values=-1 * rec)  # should be negative
o3.pattern.UniformExcitation(osi, dir=o3.cc.X, accel_series=acc_series)

# set damping based on first eigen mode
angular_freq = o3.get_eigen(osi, solver='fullGenLapack', n=1) ** 0.5
beta_k = 2 * xi / angular_freq
o3.rayleigh.Rayleigh(osi, alpha_m=0.0, beta_k=beta_k, beta_k_init=0.0, beta_k_comm=0.0)

# Run the dynamic analysis
o3.wipe_analysis(osi)

# Run the dynamic analysis
o3.algorithm.Newton(osi)
o3.system.SparseGeneral(osi)
o3.numberer.RCM(osi)
o3.constraints.Transformation(osi)
o3.integrator.Newmark(osi, gamma=0.5, beta=0.25)
o3.analysis.Transient(osi)

o3.test_check.EnergyIncr(osi, tol=1.0e-10, max_iter=10)
analysis_time = (len(rec) - 1) * dt
analysis_dt = 0.001
outputs = {
    "time": [],
    "rel_disp": [],
    "rel_accel": [],
    "rel_vel": [],
    "force": []
}

while o3.get_time(osi) < analysis_time:
    o3.analyze(osi, 1, analysis_dt)
    curr_time = o3.get_time(osi)
    outputs["time"].append(curr_time)
    outputs["rel_disp"].append(o3.get_node_disp(osi, top_node, o3.cc.X))
    outputs["rel_vel"].append(o3.get_node_vel(osi, top_node, o3.cc.X))
    outputs["rel_accel"].append(o3.get_node_accel(osi, top_node, o3.cc.X))
    o3.gen_reactions(osi)
    outputs["force"].append(-o3.get_node_reaction(osi, bot_node, o3.cc.X))  # Negative since diff node
o3.wipe(osi)
for item in outputs:
    outputs[item] = np.array(outputs[item])


plt.plot(outputs['time'], outputs['rel_disp'], label='o3seespy')
periods = np.array([period])

# Compare closed form elastic solution
from eqsig import sdof
resp_u, resp_v, resp_a = sdof.response_series(motion=rec, dt=dt, periods=periods, xi=xi)
plt.plot(np.arange(len(rec)) * dt, resp_u[0], ls='--', label='Elastic')
plt.legend()
plt.savefig('readme_example.png')
plt.show()

Useful material

Contributing

How do I get set up?

1. Run pip install -r requirements.txt

Package conventions

• All names should be the same as the OpenSees tcl version, except:

  • The name should be converted to snake_case for a parameter or function
  • The name should be converted to CamelCase for an Object
  • The name should be converted to ALL_CAPS for static variables
  • If the name matches a python special name (e.g. lambda, in) then it should be adjusted according to the dictionary
  • Objects should be namespaced based on the object type (e.g. element, material)
  • For parameter that are used across many objects (e.g. atmospheric pressure) a standard name should be used
  • OpenSees functions that collect a result are named 'get_<function-name>'
  • OpenSees functions that generate results are named 'gen_<function-name>'

• How should youngs modulus be named?
• Should all two node elements use i_node, j_node. Or make them all enter nodes as a list?

Testing

Tests are run with pytest

• Locally run: pytest on the command line.
• Tests are run on every push using travis, see the .travis.yml file

Deployment

To deploy the package to pypi.com you need to:

1. Push to the pypi branch. This executes the tests on circleci.com
2. Create a git tag and push to github, run: trigger_deploy.py or manually:

git tag 0.5.2 -m "version 0.5.2"
git push --tags origin pypi

Documentation

Built via Sphinx following: https://codeandchaos.wordpress.com/2012/07/30/sphinx-autodoc-tutorial-for-dummies/

For development mode

1. cd to docs
2. Run make html

Docstrings follow numpy convention (in progress): https://numpydoc.readthedocs.io/en/latest/format.html

To fix long_description in setup.py: pip install collective.checkdocs, python setup.py checkdocs