import os
import numpy as np
import xarray as xr
from typing import Union
import openseespy.opensees as ops
from ..utils import CONSTANTS, get_random_color
from .model_data import GetFEMData
def _get_modal_properties(mode_tag: int = 1, solver: str = "-genBandArpack"):
"""Get modal properties' data.
Parameters
----------
mode_tag : int, optional,
Modal tag, all modal data smaller than this modal tag will be saved, by default 1
solver : str, optional,
OpenSees' eigenvalue analysis solver, by default "-genBandArpack".
See `eigen Command <https://opensees.github.io/OpenSeesDocumentation/user/manual/analysis/eigen.html>`_
Returns
--------
Data: xr.DataArray, modal properties' data.
"""
ops.wipeAnalysis()
if mode_tag == 1:
ops.eigen(solver, 2)
else:
ops.eigen(solver, mode_tag)
modal_props = ops.modalProperties("-return")
# ------------------------------------------------------------------------
attrs_names = ["domainSize", "totalMass", "totalFreeMass", "centerOfMass"]
attrs = {name: tuple(modal_props[name]) for name in attrs_names}
for key, value in attrs.items():
if key == "domainSize":
value = [int(v) for v in value]
if len(value) == 1:
value = value[0]
attrs[key] = value
# ------------------------------------------------------------------------
column_names = [name for name in modal_props.keys() if name not in attrs_names]
columns = [modal_props[name] for name in column_names]
data = np.vstack(columns).transpose()[:mode_tag]
data = xr.DataArray(
data,
coords={
"modeTags": np.arange(1, mode_tag + 1),
"Properties": column_names,
},
dims=("modeTags", "Properties"),
attrs=attrs,
name="ModalProps",
)
return data
def _get_eigen_info(
mode_tag: int = 1,
solver: str = "-genBandArpack",
):
"""Get modal properties' data.
Parameters
----------
mode_tag : int, optional,
Modal tag, all modal data smaller than this modal tag will be saved, by default 1
solver : str, optional,
OpenSees' eigenvalue analysis solver, by default "-genBandArpack".
See `eigen Command <https://opensees.github.io/OpenSeesDocumentation/user/manual/analysis/eigen.html>`_
Returns
--------
modal_props: xr.DataArray
Modal properties' data.
eigenvectors: xr.DataArray
Eigen vectors data.
"""
modal_props = _get_modal_properties(mode_tag, solver)
eigenvectors = []
node_tags = ops.getNodeTags()
for mode_tag in range(1, mode_tag + 1):
eigen_vector = np.zeros((len(node_tags), 6))
for i, Tag in enumerate(node_tags):
coord = ops.nodeCoord(Tag)
eigen = ops.nodeEigenvector(Tag, mode_tag)
if len(coord) == 1:
coord.extend([0, 0])
eigen.extend([0, 0, 0, 0, 0])
elif len(coord) == 2:
coord.extend([0])
if len(eigen) == 3:
eigen = [eigen[0], eigen[1], 0, 0, 0, eigen[2]]
elif len(eigen) == 2:
eigen = [eigen[0], eigen[1], 0, 0, 0, 0]
elif len(eigen) == 1:
eigen.extend([0, 0, 0, 0, 0])
else:
if len(eigen) == 3:
eigen.extend([0, 0, 0])
elif len(eigen) > 6:
eigen = eigen[:6]
eigen_vector[i] = eigen
eigenvectors.append(eigen_vector)
eigenvectors = xr.DataArray(
eigenvectors,
dims=["modeTags", "nodeTags", "DOFs"],
coords={
"modeTags": np.arange(1, mode_tag + 1),
"nodeTags": node_tags,
"DOFs": ["UX", "UY", "UZ", "RX", "RY", "RZ"],
},
name="EigenVectors",
)
return modal_props, eigenvectors
[docs]
def save_eigen_data(
odb_tag: Union[str, int] = 1,
mode_tag: int = 1,
solver: str = "-genBandArpack",
):
"""Save modal analysis data.
Parameters
----------
odb_tag: Union[str, int], default = 1
Output database tag, the data will be saved in ``EigenData-{odb_tag}.nc``.
mode_tag : int, optional,
Modal tag, all modal data smaller than this modal tag will be saved, by default 1
solver : str, optional,
OpenSees' eigenvalue analysis solver, by default "-genBandArpack".
See `eigen Command <https://opensees.github.io/OpenSeesDocumentation/user/manual/analysis/eigen.html>`_
"""
# ----------------------------------------------------------------------
RESULTS_DIR = CONSTANTS.get_output_dir()
CONSOLE = CONSTANTS.get_console()
PKG_PREFIX = CONSTANTS.get_pkg_prefix()
output_filename = RESULTS_DIR + "/" + f"EigenData-{odb_tag}.nc"
# -----------------------------------------------------------------
model_info, _ = GetFEMData().get_model_info()
modal_props, eigen_vectors = _get_eigen_info(mode_tag, solver)
eigen_data = dict()
for key in model_info.keys():
eigen_data[f"ModelInfo/{key}"] = xr.Dataset({key: model_info[key]})
eigen_data["Eigen/ModalProps"] = xr.Dataset({modal_props.name: modal_props})
eigen_data["Eigen/EigenVectors"] = xr.Dataset({eigen_vectors.name: eigen_vectors})
dt = xr.DataTree.from_dict(eigen_data, name="EigenData")
dt.to_netcdf(output_filename, mode="w", engine="netcdf4")
# /////////////////////////////////////
color = get_random_color()
CONSOLE.print(
f"{PKG_PREFIX} Eigen data has been saved to [bold {color}]{output_filename}[/]!"
)
def load_eigen_data(
odb_tag: Union[str, int] = 1,
mode_tag: int = 1,
solver: str = "-genBandArpack",
resave: bool = True,
):
"""Get the eigenvalue data from the saved file."""
RESULTS_DIR = CONSTANTS.get_output_dir()
CONSOLE = CONSTANTS.get_console()
PKG_PREFIX = CONSTANTS.get_pkg_prefix()
filename = f"{RESULTS_DIR}/" + f"EigenData-{odb_tag}.nc"
if not os.path.exists(filename):
resave = True
if resave:
save_eigen_data(odb_tag=odb_tag, mode_tag=mode_tag, solver=solver)
else:
color = get_random_color()
CONSOLE.print(
f"{PKG_PREFIX} Loading eigen data from [bold {color}]{filename}[/] ..."
)
dt = xr.open_datatree(filename, engine="netcdf4").load()
model_info = dict()
for key, value in dt["ModelInfo"].items():
model_info[key] = value[key]
model_props = dt["Eigen/ModalProps"]["ModalProps"]
eigen_vectors = dt["Eigen/EigenVectors"]["EigenVectors"]
return model_props, eigen_vectors, model_info
[docs]
def get_eigen_data(
odb_tag: Union[str, int] = None,
):
"""Get the eigenvalue data from the saved file.
Parameters
----------
odb_tag: Union[int, str], default: None
Tag of output databases (ODB) have been saved.
Returns
--------
modal_props: xr.DataArray
Modal properties' data.
eigenvectors: xr.DataArray
Eigen vectors data.
"""
model_props, eigen_vectors, _ = load_eigen_data(odb_tag, resave=False)
return model_props, eigen_vectors