# -*- coding: utf-8 -*-
"""
This file is part of FElupe.
FElupe is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
FElupe is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with FElupe. If not, see <http://www.gnu.org/licenses/>.
"""
from copy import deepcopy
import numpy as np
from ..math import identity
from ..math import sym as symmetric
from ._container import FieldContainer
from ._indices import Indices
[docs]
class Field:
r"""A Field on points of a :class:`~felupe.Region` with dimension ``dim`` and
initial point ``values``.
Parameters
----------
region : Region
The region on which the field will be created.
dim : int, optional
The dimension of the field (default is 1).
values : float or array
A single value for all components of the field or an array of shape
`(region.mesh.npoints, dim)`. Default is 0.0.
**kwargs : dict, optional
Extra class attributes for the field.
Notes
-----
A slice of this field directly accesses the field-values as 1d-array. The
interpolation method returns the field values evaluated at the numeric integration
points ``q`` for each cell ``c`` in the region (so-called *trailing axes*).
.. math::
u_{i(qc)} = \hat{u}_{ai}\ h_{a(qc)}
The gradient method returns the gradient of the field values w.r.t. the
undeformed mesh point coordinates, evaluated at the integration points of
all cells in the region.
.. math::
\left( \frac{\partial u_i}{\partial X_J} \right)_{(qc)} =
\hat{u}_{ai} \left( \frac{\partial h_a}{\partial X_J} \right)_{(qc)}
Examples
--------
>>> import felupe as fem
>>> mesh = fem.Cube(n=6)
>>> region = fem.RegionHexahedron(mesh)
>>> displacement = fem.Field(region, dim=3)
>>> u = displacement.interpolate()
>>> dudX = displacement.grad()
To obtain deformation-related quantities like the right Cauchy-Green deformation
tensor or the principal stretches, use the math-helpers from FElupe. These
functions operate on arrays with trailing axes.
.. math::
\boldsymbol{C} = \boldsymbol{F}^T \boldsymbol{F}
>>> from felupe.math import dot, transpose, eigvalsh, sqrt
>>> F = displacement.extract(grad=True, add_identity=True)
>>> C = dot(transpose(F), F)
>>> λ = sqrt(eigvalsh(C))
"""
def __init__(self, region, dim=1, values=0.0, **kwargs):
self.region = region
self.dim = dim
self.shape = self.region.quadrature.npoints, self.region.mesh.ncells
# set optional user-defined attributes
for key, value in kwargs.items():
setattr(self, key, value)
# init values
if isinstance(values, np.ndarray):
if values.size == dim:
shape = (region.mesh.npoints, dim)
self.values = np.broadcast_to(values.reshape(-1, dim), shape)
elif len(values) == region.mesh.npoints:
self.values = values
else:
raise ValueError("Wrong shape of values.")
else: # scalar value
self.values = np.ones((region.mesh.npoints, dim)) * values
eai, ai = self._indices_per_cell(self.region.mesh.cells, dim)
self.indices = Indices(eai, ai, region, dim)
def _indices_per_cell(self, cells, dim):
"Calculate pre-defined indices for sparse matrices."
# index of cell "c", point "a" and component "i"
cai = (
dim * np.repeat(cells, dim) + np.tile(np.arange(dim), cells.size)
).reshape(*cells.shape, dim)
# store indices as (rows, cols) (note: sparse-matrices are always 2d)
ai = (cai.ravel(), np.zeros_like(cai.ravel()))
return cai, ai
[docs]
def grad(self, sym=False, out=None):
r"""Gradient as partial derivative of field values w.r.t. undeformed
coordinates, evaluated at the integration points of all cells in the region.
Optionally, the symmetric part the gradient is evaluated.
.. math::
\left( \frac{\partial u_i}{\partial X_J} \right)_{(qc)} =
\hat{u}_{ai} \left( \frac{\partial h_a}{\partial X_J} \right)_{(qc)}
Arguments
---------
sym : bool, optional
Calculate the symmetric part of the gradient (default is False).
out : None or ndarray, optional
A location into which the result is stored. If provided, it must have a
shape that the inputs broadcast to. If not provided or None, a freshly-
allocated array is returned (default is None).
Returns
-------
ndarray of shape (i, j, q, c)
Gradient as partial derivative of field value components ``i`` at points
w.r.t. the undeformed coordinates ``j``, evaluated at the quadrature points
``q`` of cells ``c`` in the region.
"""
# gradient dudX_IJqc as partial derivative of field values at points "aI"
# w.r.t. undeformed coordinates "J" evaluated at quadrature point "q"
# for each cell "c"
g = np.einsum(
"ca...,aJqc->...Jqc",
self.values[self.region.mesh.cells],
self.region.dhdX,
out=out,
)
if sym:
return symmetric(g)
else:
return g
[docs]
def interpolate(self, out=None):
"""Interpolate field values located at mesh-points to the quadrature points
``q`` of cells ``c`` in the region.
.. math::
u_{i(qc)} = \hat{u}_{ai}\ h_{a(qc)}
Arguments
---------
out : None or ndarray, optional
A location into which the result is stored. If provided, it must have a
shape that the inputs broadcast to. If not provided or None, a freshly-
allocated array is returned (default is None).
Returns
-------
ndarray of shape (i, q, c)
Interpolated field value components ``i``, evaluated at the quadrature
points ``q`` of each cell ``c`` in the region.
"""
# interpolated field values "aI"
# evaluated at quadrature point "q"
# for cell "c"
return np.einsum(
"ca...,aqc->...qc",
self.values[self.region.mesh.cells],
self.region.h,
out=None,
)
[docs]
def copy(self):
"Return a copy of the field."
return deepcopy(self)
[docs]
def fill(self, a):
"Fill all field values with a scalar value."
self.values.fill(a)
[docs]
def as_container(self):
"Create a :class:`~felupe.FieldContainer` with the field."
return FieldContainer([self])
def __add__(self, newvalues):
if isinstance(newvalues, np.ndarray):
field = deepcopy(self)
field.values += newvalues.reshape(-1, field.dim)
return field
elif isinstance(newvalues, Field):
field = deepcopy(self)
field.values += newvalues.values
return field
else:
raise TypeError("Unknown type.")
def __sub__(self, newvalues):
if isinstance(newvalues, np.ndarray):
field = deepcopy(self)
field.values -= newvalues.reshape(-1, field.dim)
return field
elif isinstance(newvalues, Field):
field = deepcopy(self)
field.values -= newvalues.values
return field
else:
raise TypeError("Unknown type.")
def __mul__(self, newvalues):
if isinstance(newvalues, np.ndarray):
field = deepcopy(self)
field.values *= newvalues.reshape(-1, field.dim)
return field
elif isinstance(newvalues, Field):
field = deepcopy(self)
field.values *= newvalues.values
return field
else:
raise TypeError("Unknown type.")
def __truediv__(self, newvalues):
if isinstance(newvalues, np.ndarray):
field = deepcopy(self)
field.values /= newvalues.reshape(-1, field.dim)
return field
elif isinstance(newvalues, Field):
field = deepcopy(self)
field.values /= newvalues.values
return field
else:
raise TypeError("Unknown type.")
def __iadd__(self, newvalues):
if isinstance(newvalues, np.ndarray):
self.values += newvalues.reshape(-1, self.dim)
return self
elif isinstance(newvalues, Field):
self.values += newvalues.values
return self
else:
raise TypeError("Unknown type.")
def __isub__(self, newvalues):
if isinstance(newvalues, np.ndarray):
self.values -= newvalues.reshape(-1, self.dim)
return self
elif isinstance(newvalues, Field):
self.values -= newvalues.values
return self
else:
raise TypeError("Unknown type.")
def __imul__(self, newvalues):
if isinstance(newvalues, np.ndarray):
self.values *= newvalues.reshape(-1, self.dim)
return self
elif isinstance(newvalues, Field):
self.values *= newvalues.values
return self
else:
raise TypeError("Unknown type.")
def __itruediv__(self, newvalues):
if isinstance(newvalues, np.ndarray):
self.values /= newvalues.reshape(-1, self.dim)
return self
elif isinstance(newvalues, Field):
self.values /= newvalues.values
return self
else:
raise TypeError("Unknown type.")
def __getitem__(self, dof):
"""Slice-based access to a 1d-representation of field values by
a list of degrees of freedom `dof`."""
return self.values.ravel()[dof]
def __and__(self, field):
fields = [field]
if isinstance(field, FieldContainer):
fields = field.fields
elif field is None:
fields = []
return FieldContainer([self, *fields])
