fipy package

Subpackages

Submodules

fipy.testFiPy module

Module contents

FiPy is an object oriented, partial differential equation (PDE) solver, written in Python, based on a standard finite volume (FV) approach. The framework has been developed in the Materials Science and Engineering Division (MSED) and Center for Theoretical and Computational Materials Science (CTCMS), in the Material Measurement Laboratory (MML) at the National Institute of Standards and Technology (NIST).

The solution of coupled sets of PDEs is ubiquitous to the numerical simulation of science problems. Numerous PDE solvers exist, using a variety of languages and numerical approaches. Many are proprietary, expensive and difficult to customize. As a result, scientists spend considerable resources repeatedly developing limited tools for specific problems. Our approach, combining the FV method and Python, provides a tool that is extensible, powerful and freely available. A significant advantage to Python is the existing suite of tools for array calculations, sparse matrices and data rendering.

The FiPy framework includes terms for transient diffusion, convection and standard sources, enabling the solution of arbitrary combinations of coupled elliptic, hyperbolic and parabolic PDEs. Currently implemented models include phase field [1] [2] [3] treatments of polycrystalline, dendritic, and electrochemical phase transformations, as well as drug eluting stents [4], reactive wetting [5], photovoltaics [6] and a level set treatment of the electrodeposition process [7].

exception fipy.AbstractBaseClassError(s="can't instantiate abstract base class")

Bases: NotImplementedError

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class fipy.AdvectionTerm(coeff=None)

Bases: FirstOrderAdvectionTerm

The AdvectionTerm object constructs the b vector contribution for the advection term given by

u \abs{\nabla \phi}

from the advection equation given by:

\frac{\partial \phi}{\partial t} + u \abs{\nabla \phi} = 0

The construction of the gradient magnitude term requires upwinding as in the standard FirstOrderAdvectionTerm. The higher order terms are incorporated as follows. The formula used here is given by:

u_P \abs{\nabla \phi}_P = \max \left( u_P , 0 \right) \left[ \sum_A \min \left( D_{AP}, 0 \right)^2 \right]^{1/2} + \min \left( u_P , 0 \right) \left[ \sum_A \max \left( D_{AP}, 0 \right)^2 \right]^{1/2}

where,

D_{AP} = \frac{ \phi_A - \phi_P } { d_{AP}} - \frac{ d_{AP} } {2} m \left(L_A, L_P \right)

and

m\left(x, y\right) &= x \qquad \text{if $\abs{x} \le \abs{y} \forall xy \ge 0$} \\ m\left(x, y\right) &= y \qquad \text{if $\abs{x} > \abs{y} \forall xy \ge 0$} \\ m\left(x, y\right) &= 0 \qquad \text{if $xy < 0$}

also,

L_A &= \frac{\phi_{AA} + \phi_P - 2 \phi_A}{d_{AP}^2} \\ L_P &= \frac{\phi_{A} + \phi_{PP} - 2 \phi_P}{d_{AP}^2}

Here are some simple test cases for this problem:

>>> from fipy.meshes import Grid1D
>>> from fipy.solvers import *
>>> SparseMatrix = LinearPCGSolver()._matrixClass
>>> mesh = Grid1D(dx = 1., nx = 3)

Trivial test:

>>> from fipy.variables.cellVariable import CellVariable
>>> coeff = CellVariable(mesh = mesh, value = numerix.zeros(3, 'd'))
>>> v, L, b = AdvectionTerm(0.)._buildMatrix(coeff, SparseMatrix)
>>> print(numerix.allclose(b, numerix.zeros(3, 'd'), atol = 1e-10)) 
True

Less trivial test:

>>> coeff = CellVariable(mesh = mesh, value = numerix.arange(3))
>>> v, L, b = AdvectionTerm(1.)._buildMatrix(coeff, SparseMatrix)
>>> print(numerix.allclose(b, numerix.array((0., -1., -1.)), atol = 1e-10)) 
True

Even less trivial

>>> coeff = CellVariable(mesh = mesh, value = numerix.arange(3))
>>> v, L, b = AdvectionTerm(-1.)._buildMatrix(coeff, SparseMatrix)
>>> print(numerix.allclose(b, numerix.array((1., 1., 0.)), atol = 1e-10)) 
True

Another trivial test case (more trivial than a trivial test case standing on a harpsichord singing “trivial test cases are here again”)

>>> vel = numerix.array((-1, 2, -3))
>>> coeff = CellVariable(mesh = mesh, value = numerix.array((4, 6, 1)))
>>> v, L, b = AdvectionTerm(vel)._buildMatrix(coeff, SparseMatrix)
>>> print(numerix.allclose(b, -vel * numerix.array((2, numerix.sqrt(5**2 + 2**2), 5)), atol = 1e-10)) 
True

Somewhat less trivial test case:

>>> from fipy.meshes import Grid2D
>>> mesh = Grid2D(dx = 1., dy = 1., nx = 2, ny = 2)
>>> vel = numerix.array((3, -5, -6, -3))
>>> coeff = CellVariable(mesh = mesh, value = numerix.array((3, 1, 6, 7)))
>>> v, L, b = AdvectionTerm(vel)._buildMatrix(coeff, SparseMatrix)
>>> answer = -vel * numerix.array((2, numerix.sqrt(2**2 + 6**2), 1, 0))
>>> print(numerix.allclose(b, answer, atol = 1e-10)) 
True

For the above test cases the AdvectionTerm gives the same result as the AdvectionTerm. The following test imposes a quadratic field. The higher order term can resolve this field correctly.

\phi = x^2

The returned vector b should have the value:

-\abs{\nabla \phi} = -\left|\frac{\partial \phi}{\partial x}\right| = - 2 \abs{x}

Build the test case in the following way,

>>> mesh = Grid1D(dx = 1., nx = 5)
>>> vel = 1.
>>> coeff = CellVariable(mesh = mesh, value = mesh.cellCenters[0]**2)
>>> v, L, b = __AdvectionTerm(vel)._buildMatrix(coeff, SparseMatrix)

The first order term is not accurate. The first and last element are ignored because they don’t have any neighbors for higher order evaluation

>>> print(numerix.allclose(CellVariable(mesh=mesh,
... value=b).globalValue[1:-1], -2 * mesh.cellCenters.globalValue[0][1:-1]))
False

The higher order term is spot on.

>>> v, L, b = AdvectionTerm(vel)._buildMatrix(coeff, SparseMatrix)
>>> print(numerix.allclose(CellVariable(mesh=mesh,
... value=b).globalValue[1:-1], -2 * mesh.cellCenters.globalValue[0][1:-1]))
True

The AdvectionTerm will also resolve a circular field with more accuracy,

\phi = \left( x^2 + y^2 \right)^{1/2}

Build the test case in the following way,

>>> mesh = Grid2D(dx = 1., dy = 1., nx = 10, ny = 10)
>>> vel = 1.
>>> x, y = mesh.cellCenters
>>> r = numerix.sqrt(x**2 + y**2)
>>> coeff = CellVariable(mesh = mesh, value = r)
>>> v, L, b = __AdvectionTerm(1.)._buildMatrix(coeff, SparseMatrix)
>>> error = CellVariable(mesh=mesh, value=b + 1)
>>> ans = CellVariable(mesh=mesh, value=b + 1)
>>> ans[(x > 2) & (x < 8) & (y > 2) & (y < 8)] = 0.123105625618
>>> print((error <= ans).all())
True

The maximum error is large (about 12 %) for the first order advection.

>>> v, L, b = AdvectionTerm(1.)._buildMatrix(coeff, SparseMatrix)
>>> error = CellVariable(mesh=mesh, value=b + 1)
>>> ans = CellVariable(mesh=mesh, value=b + 1)
>>> ans[(x > 2) & (x < 8) & (y > 2) & (y < 8)] = 0.0201715476598
>>> print((error <= ans).all())
True

The maximum error is 2 % when using a higher order contribution.

Create a Term.

Parameters:

coeff (float or CellVariable or FaceVariable) – Coefficient for the term. FaceVariable objects are only acceptable for diffusion or convection terms.

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

class fipy.BetaNoiseVariable(*args, **kwds)

Bases: NoiseVariable

Represents a beta distribution of random numbers with the probability distribution

x^{\alpha - 1}\frac{\beta^\alpha e^{-\beta x}}{\Gamma(\alpha)}

with a shape parameter \alpha, a rate parameter \beta, and \Gamma(z) = \int_0^\infty t^{z - 1}e^{-t}\,dt.

Seed the random module for the sake of deterministic test results.

>>> from fipy import numerix
>>> numerix.random.seed(1)

We generate noise on a uniform Cartesian mesh

>>> from fipy.variables.variable import Variable
>>> alpha = Variable()
>>> beta = Variable()
>>> from fipy.meshes import Grid2D
>>> noise = BetaNoiseVariable(mesh = Grid2D(nx = 100, ny = 100), alpha = alpha, beta = beta)

We histogram the root-volume-weighted noise distribution

>>> from fipy.variables.histogramVariable import HistogramVariable
>>> histogram = HistogramVariable(distribution = noise, dx = 0.01, nx = 100)

and compare to a Gaussian distribution

>>> from fipy.variables.cellVariable import CellVariable
>>> betadist = CellVariable(mesh = histogram.mesh)
>>> x = CellVariable(mesh=histogram.mesh, value=histogram.mesh.cellCenters[0])
>>> from scipy.special import gamma as Gamma 
>>> betadist = ((Gamma(alpha + beta) / (Gamma(alpha) * Gamma(beta)))
...             * x**(alpha - 1) * (1 - x)**(beta - 1)) 

>>> if __name__ == '__main__':
...     from fipy import Viewer
...     viewer = Viewer(vars=noise, datamin=0, datamax=1)
...     histoplot = Viewer(vars=(histogram, betadist),
...                        datamin=0, datamax=1.5)

>>> from fipy.tools.numerix import arange

>>> for a in arange(0.5, 5, 0.5):
...     alpha.value = a
...     for b in arange(0.5, 5, 0.5):
...         beta.value = b
...         if __name__ == '__main__':
...             import sys
...             print("alpha: %g, beta: %g" % (alpha, beta), file=sys.stderr)
...             viewer.plot()
...             histoplot.plot()

>>> print(abs(noise.faceGrad.divergence.cellVolumeAverage) < 5e-15)
1
random values with a beta distribution histogram of random values with a beta distribution
Parameters:

  • mesh (Mesh) – The mesh on which to define the noise.

  • alpha (float) – The parameter \alpha.

  • beta (float) – The parameter \beta.

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__(points=None, order=0, nearestCellIDs=None)

Interpolates the CellVariable to a set of points using a method that has a memory requirement on the order of Ncells by Npoints in general, but uses only Ncells when the CellVariable’s mesh is a UniformGrid object.

Tests

>>> from fipy import *
>>> m = Grid2D(nx=3, ny=2)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.))))
[ 0.5  1.5  1.5]
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.)), order=1))
[ 0.25  1.1   1.2 ]
>>> m0 = Grid2D(nx=2, ny=2, dx=1., dy=1.)
>>> m1 = Grid2D(nx=4, ny=4, dx=.5, dy=.5)
>>> x, y = m0.cellCenters
>>> v0 = CellVariable(mesh=m0, value=x * y)
>>> print(v0(m1.cellCenters.globalValue))
[ 0.25  0.25  0.75  0.75  0.25  0.25  0.75  0.75  0.75  0.75  2.25  2.25
  0.75  0.75  2.25  2.25]
>>> print(v0(m1.cellCenters.globalValue, order=1))
[ 0.125  0.25   0.5    0.625  0.25   0.375  0.875  1.     0.5    0.875
  1.875  2.25   0.625  1.     2.25   2.625]
Parameters:

  • points (tuple or list of tuple) – A point or set of points in the format (X, Y, Z)

  • order ({0, 1}) – The order of interpolation, default is 0

  • nearestCellIDs (array_like) – Optional argument if user can calculate own nearest cell IDs array, shape should be same as points

__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the CellVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new CellVariable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
property arithmeticFaceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
cacheMe(recursive=False)
property cellVolumeAverage

Return the cell-volume-weighted average of the CellVariable:

<\phi>_\text{vol} = \frac{\sum_\text{cells} \phi_\text{cell} V_\text{cell}} {\sum_\text{cells} V_\text{cell}}

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx = 3, ny = 1, dx = .5, dy = .1)
>>> var = CellVariable(value = (1, 2, 6), mesh = mesh)
>>> print(var.cellVolumeAverage)
3.0
constrain(value, where=None)

Constrains the CellVariable to value at a location specified by where.

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> v.constrain(0., where=m.facesLeft)
>>> v.faceGrad.constrain([1.], where=m.facesRight)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]

Changing the constraint changes the dependencies

>>> v.constrain(1., where=m.facesLeft)
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can be Variable

>>> c = Variable(0.)
>>> v.constrain(c, where=m.facesLeft)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> c.value = 1.
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can have a Variable mask.

>>> v = CellVariable(mesh=m)
>>> mask = FaceVariable(mesh=m, value=m.facesLeft)
>>> v.constrain(1., where=mask)
>>> print(v.faceValue)
[ 1.  0.  0.  0.]
>>> mask[:] = mask | m.facesRight
>>> print(v.faceValue)
[ 1.  0.  0.  1.]
property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Copy the value of the NoiseVariable to a static CellVariable.

dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

property faceGrad

Return \nabla \phi as a rank-1 FaceVariable using differencing for the normal direction(second-order gradient).

property faceGradAverage

Deprecated since version 3.3: use grad.arithmeticFaceValue() instead

Return \nabla \phi as a rank-1 FaceVariable using averaging for the normal direction(second-order gradient)

property faceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
property gaussGrad

Return \frac{1}{V_P} \sum_f \vec{n} \phi_f A_f as a rank-1 CellVariable (first-order gradient).

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue

Concatenate and return values from all processors

When running on a single processor, the result is identical to value.

property grad

Return \nabla \phi as a rank-1 CellVariable (first-order gradient).

property harmonicFaceValue

Returns a FaceVariable whose value corresponds to the harmonic interpolation of the adjacent cells:

\phi_f = \frac{\phi_1 \phi_2}{(\phi_2 - \phi_1) \frac{d_{f2}}{d_{12}} + \phi_1}

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (0.5 / 1.) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (1.0 / 3.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (5.0 / 55.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property leastSquaresGrad

Return \nabla \phi, which is determined by solving for \nabla \phi in the following matrix equation,

\nabla \phi \cdot \sum_f d_{AP}^2 \vec{n}_{AP} \otimes \vec{n}_{AP} = \sum_f d_{AP}^2 \left( \vec{n} \cdot \nabla \phi \right)_{AP}

The matrix equation is derived by minimizing the following least squares sum,

F \left( \phi_x, \phi_y \right) = \sqrt{\sum_f \left( d_{AP} \vec{n}_{AP} \cdot \nabla \phi - d_{AP} \left( \vec{n}_{AP} \cdot \nabla \phi \right)_{AP} \right)^2 }

Tests

>>> from fipy import Grid2D
>>> m = Grid2D(nx=2, ny=2, dx=0.1, dy=2.0)
>>> print(numerix.allclose(CellVariable(mesh=m, value=(0, 1, 3, 6)).leastSquaresGrad.globalValue, \
...                                     [[8.0, 8.0, 24.0, 24.0],
...                                      [1.2, 2.0, 1.2, 2.0]]))
True

>>> from fipy import Grid1D
>>> print(numerix.allclose(CellVariable(mesh=Grid1D(dx=(2.0, 1.0, 0.5)),
...                                     value=(0, 1, 2)).leastSquaresGrad.globalValue, [[0.461538461538, 0.8, 1.2]]))
True
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property minmodFaceValue

Returns a FaceVariable with a value that is the minimum of the absolute values of the adjacent cells. If the values are of opposite sign then the result is zero:

\phi_f = \begin{cases} \phi_1& \text{when $|\phi_1| \le |\phi_2|$},\\ \phi_2& \text{when $|\phi_2| < |\phi_1|$},\\ 0 & \text{when $\phi1 \phi2 < 0$} \end{cases}

>>> from fipy import *
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(1, 2)).minmodFaceValue)
[1 1 2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, -2)).minmodFaceValue)
[-1 -1 -2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, 2)).minmodFaceValue)
[-1  0  2]
property name
property numericValue
property old

Return the values of the CellVariable from the previous solution sweep.

Combinations of CellVariable’s should also return old values.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 2)
>>> from fipy.variables.cellVariable import CellVariable
>>> var1 = CellVariable(mesh = mesh, value = (2, 3), hasOld = 1)
>>> var2 = CellVariable(mesh = mesh, value = (3, 4))
>>> v = var1 * var2
>>> print(v)
[ 6 12]
>>> var1.value = ((3, 2))
>>> print(v)
[9 8]
>>> print(v.old)
[ 6 12]

The following small test is to correct for a bug when the operator does not just use variables.

>>> v1 = var1 * 3
>>> print(v1)
[9 6]
>>> print(v1.old)
[6 9]
parallelRandom()
put(indices, value)
random()
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> c = Constraint(0., where=m.facesLeft)
>>> v.constrain(c)
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> v.release(constraint=c)
>>> print(v.faceValue)
[ 0.5  1.   2.   2.5]
scramble()

Generate a new random distribution.

setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
updateOld()

Set the values of the previous solution sweep to the current values.

>>> from fipy import *
>>> v = CellVariable(mesh=Grid1D(), hasOld=False)
>>> v.updateOld()
Traceback (most recent call last):
   ...
AssertionError: The updateOld method requires the CellVariable to have an old value. Set hasOld to True when instantiating the CellVariable.
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
class fipy.CellVariable(*args, **kwds)

Bases: _MeshVariable

Represents the field of values of a variable on a Mesh.

A CellVariable can be pickled to persistent storage (disk) for later use:

>>> from fipy.meshes import Grid2D
>>> mesh = Grid2D(dx = 1., dy = 1., nx = 10, ny = 10)

>>> var = CellVariable(mesh = mesh, value = 1., hasOld = 1, name = 'test')
>>> x, y = mesh.cellCenters
>>> var.value = (x * y)

>>> from fipy.tools import dump
>>> (f, filename) = dump.write(var, extension = '.gz')
>>> unPickledVar = dump.read(filename, f)

>>> print(var.allclose(unPickledVar, atol = 1e-10, rtol = 1e-10))
1
Parameters:

  • mesh (Mesh) – the mesh that defines the geometry of this Variable

  • name (str) – the user-readable name of the Variable

  • value (float or array_like) – the initial value

  • rank (int) – the rank (number of dimensions) of each element of this Variable. Default: 0

  • elementshape (tuple of int) – the shape of each element of this variable Default: rank * (mesh.dim,)

  • unit (str or PhysicalUnit) – The physical units of the variable

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__(points=None, order=0, nearestCellIDs=None)

Interpolates the CellVariable to a set of points using a method that has a memory requirement on the order of Ncells by Npoints in general, but uses only Ncells when the CellVariable’s mesh is a UniformGrid object.

Tests

>>> from fipy import *
>>> m = Grid2D(nx=3, ny=2)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.))))
[ 0.5  1.5  1.5]
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.)), order=1))
[ 0.25  1.1   1.2 ]
>>> m0 = Grid2D(nx=2, ny=2, dx=1., dy=1.)
>>> m1 = Grid2D(nx=4, ny=4, dx=.5, dy=.5)
>>> x, y = m0.cellCenters
>>> v0 = CellVariable(mesh=m0, value=x * y)
>>> print(v0(m1.cellCenters.globalValue))
[ 0.25  0.25  0.75  0.75  0.25  0.25  0.75  0.75  0.75  0.75  2.25  2.25
  0.75  0.75  2.25  2.25]
>>> print(v0(m1.cellCenters.globalValue, order=1))
[ 0.125  0.25   0.5    0.625  0.25   0.375  0.875  1.     0.5    0.875
  1.875  2.25   0.625  1.     2.25   2.625]
Parameters:

  • points (tuple or list of tuple) – A point or set of points in the format (X, Y, Z)

  • order ({0, 1}) – The order of interpolation, default is 0

  • nearestCellIDs (array_like) – Optional argument if user can calculate own nearest cell IDs array, shape should be same as points

__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the CellVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new CellVariable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
property arithmeticFaceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
cacheMe(recursive=False)
property cellVolumeAverage

Return the cell-volume-weighted average of the CellVariable:

<\phi>_\text{vol} = \frac{\sum_\text{cells} \phi_\text{cell} V_\text{cell}} {\sum_\text{cells} V_\text{cell}}

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx = 3, ny = 1, dx = .5, dy = .1)
>>> var = CellVariable(value = (1, 2, 6), mesh = mesh)
>>> print(var.cellVolumeAverage)
3.0
constrain(value, where=None)

Constrains the CellVariable to value at a location specified by where.

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> v.constrain(0., where=m.facesLeft)
>>> v.faceGrad.constrain([1.], where=m.facesRight)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]

Changing the constraint changes the dependencies

>>> v.constrain(1., where=m.facesLeft)
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can be Variable

>>> c = Variable(0.)
>>> v.constrain(c, where=m.facesLeft)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> c.value = 1.
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can have a Variable mask.

>>> v = CellVariable(mesh=m)
>>> mask = FaceVariable(mesh=m, value=m.facesLeft)
>>> v.constrain(1., where=mask)
>>> print(v.faceValue)
[ 1.  0.  0.  0.]
>>> mask[:] = mask | m.facesRight
>>> print(v.faceValue)
[ 1.  0.  0.  1.]
property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Make an duplicate of the Variable

>>> a = Variable(value=3)
>>> b = a.copy()
>>> b
Variable(value=array(3))

The duplicate will not reflect changes made to the original

>>> a.setValue(5)
>>> b
Variable(value=array(3))

Check that this works for arrays.

>>> a = Variable(value=numerix.array((0, 1, 2)))
>>> b = a.copy()
>>> b
Variable(value=array([0, 1, 2]))
>>> a[1] = 3
>>> b
Variable(value=array([0, 1, 2]))
dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

property faceGrad

Return \nabla \phi as a rank-1 FaceVariable using differencing for the normal direction(second-order gradient).

property faceGradAverage

Deprecated since version 3.3: use grad.arithmeticFaceValue() instead

Return \nabla \phi as a rank-1 FaceVariable using averaging for the normal direction(second-order gradient)

property faceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
property gaussGrad

Return \frac{1}{V_P} \sum_f \vec{n} \phi_f A_f as a rank-1 CellVariable (first-order gradient).

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue

Concatenate and return values from all processors

When running on a single processor, the result is identical to value.

property grad

Return \nabla \phi as a rank-1 CellVariable (first-order gradient).

property harmonicFaceValue

Returns a FaceVariable whose value corresponds to the harmonic interpolation of the adjacent cells:

\phi_f = \frac{\phi_1 \phi_2}{(\phi_2 - \phi_1) \frac{d_{f2}}{d_{12}} + \phi_1}

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (0.5 / 1.) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (1.0 / 3.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (5.0 / 55.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property leastSquaresGrad

Return \nabla \phi, which is determined by solving for \nabla \phi in the following matrix equation,

\nabla \phi \cdot \sum_f d_{AP}^2 \vec{n}_{AP} \otimes \vec{n}_{AP} = \sum_f d_{AP}^2 \left( \vec{n} \cdot \nabla \phi \right)_{AP}

The matrix equation is derived by minimizing the following least squares sum,

F \left( \phi_x, \phi_y \right) = \sqrt{\sum_f \left( d_{AP} \vec{n}_{AP} \cdot \nabla \phi - d_{AP} \left( \vec{n}_{AP} \cdot \nabla \phi \right)_{AP} \right)^2 }

Tests

>>> from fipy import Grid2D
>>> m = Grid2D(nx=2, ny=2, dx=0.1, dy=2.0)
>>> print(numerix.allclose(CellVariable(mesh=m, value=(0, 1, 3, 6)).leastSquaresGrad.globalValue, \
...                                     [[8.0, 8.0, 24.0, 24.0],
...                                      [1.2, 2.0, 1.2, 2.0]]))
True

>>> from fipy import Grid1D
>>> print(numerix.allclose(CellVariable(mesh=Grid1D(dx=(2.0, 1.0, 0.5)),
...                                     value=(0, 1, 2)).leastSquaresGrad.globalValue, [[0.461538461538, 0.8, 1.2]]))
True
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property minmodFaceValue

Returns a FaceVariable with a value that is the minimum of the absolute values of the adjacent cells. If the values are of opposite sign then the result is zero:

\phi_f = \begin{cases} \phi_1& \text{when $|\phi_1| \le |\phi_2|$},\\ \phi_2& \text{when $|\phi_2| < |\phi_1|$},\\ 0 & \text{when $\phi1 \phi2 < 0$} \end{cases}

>>> from fipy import *
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(1, 2)).minmodFaceValue)
[1 1 2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, -2)).minmodFaceValue)
[-1 -1 -2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, 2)).minmodFaceValue)
[-1  0  2]
property name
property numericValue
property old

Return the values of the CellVariable from the previous solution sweep.

Combinations of CellVariable’s should also return old values.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 2)
>>> from fipy.variables.cellVariable import CellVariable
>>> var1 = CellVariable(mesh = mesh, value = (2, 3), hasOld = 1)
>>> var2 = CellVariable(mesh = mesh, value = (3, 4))
>>> v = var1 * var2
>>> print(v)
[ 6 12]
>>> var1.value = ((3, 2))
>>> print(v)
[9 8]
>>> print(v.old)
[ 6 12]

The following small test is to correct for a bug when the operator does not just use variables.

>>> v1 = var1 * 3
>>> print(v1)
[9 6]
>>> print(v1.old)
[6 9]
put(indices, value)
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> c = Constraint(0., where=m.facesLeft)
>>> v.constrain(c)
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> v.release(constraint=c)
>>> print(v.faceValue)
[ 0.5  1.   2.   2.5]
setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
updateOld()

Set the values of the previous solution sweep to the current values.

>>> from fipy import *
>>> v = CellVariable(mesh=Grid1D(), hasOld=False)
>>> v.updateOld()
Traceback (most recent call last):
   ...
AssertionError: The updateOld method requires the CellVariable to have an old value. Set hasOld to True when instantiating the CellVariable.
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
class fipy.CentralDifferenceConvectionTerm(coeff=1.0, var=None)

Bases: _AbstractConvectionTerm

This Term represents

\int_V \nabla \cdot (\vec{u} \phi)\,dV \simeq \sum_{f} (\vec{n} \cdot \vec{u})_f \phi_f A_f

where \phi_f=\alpha_f \phi_P +(1-\alpha_f)\phi_A and \alpha_f is calculated using the central differencing scheme. For further details see Numerical Schemes.

Create a _AbstractConvectionTerm object.

>>> from fipy import *
>>> m = Grid1D(nx = 2)
>>> cv = CellVariable(mesh = m)
>>> fv = FaceVariable(mesh = m)
>>> vcv = CellVariable(mesh=m, rank=1)
>>> vfv = FaceVariable(mesh=m, rank=1)
>>> __ConvectionTerm(coeff = cv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = fv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = vcv)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = vfv)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = (1,))
__ConvectionTerm(coeff=(1,))
>>> ExplicitUpwindConvectionTerm(coeff = (0,)).solve(var=cv, solver=DummySolver()) 
Traceback (most recent call last):
    ...
TransientTermError: The equation requires a TransientTerm with explicit convection.
>>> (TransientTerm(0.) - ExplicitUpwindConvectionTerm(coeff = (0,))).solve(var=cv, solver=DummySolver(), dt=1.)

>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = 1)).solve(var=cv, solver=DummySolver(), dt=1.) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> m2 = Grid2D(nx=2, ny=1)
>>> cv2 = CellVariable(mesh=m2)
>>> vcv2 = CellVariable(mesh=m2, rank=1)
>>> vfv2 = FaceVariable(mesh=m2, rank=1)
>>> __ConvectionTerm(coeff=vcv2)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> __ConvectionTerm(coeff=vfv2)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = ((0,), (0,)))).solve(var=cv2, solver=DummySolver(), dt=1.)
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = (0, 0))).solve(var=cv2, solver=DummySolver(), dt=1.)
Parameters:

coeff (The Term’s coefficient value.) –

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

class fipy.Constraint(value, where=None)

Bases: object

Object to hold a Variable to value at where

see constrain()

__repr__()

Return repr(self).

fipy.ConvectionTerm

alias of PowerLawConvectionTerm

fipy.CylindricalGrid1D(dr=None, nr=None, Lr=None, dx=1.0, nx=None, Lx=None, origin=(0,), overlap=2, communicator=SerialPETScCommWrapper())

Create a 2D cylindrical mesh

Factory function to select between CylindricalUniformGrid1D and CylindricalNonUniformGrid1D. If Lr is specified the length of the domain is always Lr regardless of dr, unless dr is a list of spacings, in which case Lr will be the sum of dr.

Parameters:

  • dr (float) – Grid spacing in the radial direction. Alternative: dx.

  • nr (int) – Number of cells in the radial direction. Alternative: nx.

  • Lr (float) – Domain length in the radial direction. Alternative: Lx.

  • overlap (int) – the number of overlapping cells for parallel simulations. Generally 2 is adequate. Higher order equations or discretizations require more.

  • communicator (CommWrapper) – MPI communicator to use. Select serialComm to create a serial mesh when running in parallel; mostly used for test purposes. (default: parallelComm).

fipy.CylindricalGrid2D(dr=None, dz=None, nr=None, nz=None, Lr=None, Lz=None, dx=1.0, dy=1.0, nx=None, ny=None, Lx=None, Ly=None, origin=((0,), (0,)), overlap=2, communicator=SerialPETScCommWrapper())

Create a 2D cylindrical mesh

Factory function to select between CylindricalUniformGrid2D and CylindricalNonUniformGrid2D. If Lr is specified the length of the domain is always Lr regardless of dr, unless dr is a list of spacings, in which case Lr will be the sum of dr.

Parameters:

  • dr (float) – Grid spacing in the radial direction. Alternative: dx.

  • dz (float) – grid spacing in the vertical direction. Alternative: dy.

  • nr (int) – Number of cells in the radial direction. Alternative: nx.

  • nz (int) – Number of cells in the vertical direction. Alternative: ny.

  • Lr (float) – Domain length in the radial direction. Alternative: Lx.

  • Lz (float) – Domain length in the vertical direction. Alternative: Ly.

  • overlap (int) – the number of overlapping cells for parallel simulations. Generally 2 is adequate. Higher order equations or discretizations require more.

  • communicator (CommWrapper) – MPI communicator to use. Select serialComm to create a serial mesh when running in parallel; mostly used for test purposes. (default: parallelComm).

fipy.DefaultAsymmetricSolver

alias of LinearGMRESSolver

fipy.DefaultSolver

alias of LinearGMRESSolver

class fipy.DiffusionTerm(coeff=(1.0,), var=None)

Bases: DiffusionTermNoCorrection

This term represents a higher order diffusion term. The order of the term is determined by the number of coeffs, such that:

DiffusionTerm(D1)

represents a typical 2nd-order diffusion term of the form

\nabla\cdot\left(D_1 \nabla \phi\right)

and:

DiffusionTerm((D1,D2))

represents a 4th-order Cahn-Hilliard term of the form

\nabla \cdot \left\{ D_1 \nabla \left[ \nabla\cdot\left( D_2 \nabla \phi\right) \right] \right\}

and so on.

Create a Term.

Parameters:

coeff (float or CellVariable or FaceVariable) – Coefficient for the term. FaceVariable objects are only acceptable for diffusion or convection terms.

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)
__neg__()
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

class fipy.DiffusionTermCorrection(coeff=(1.0,), var=None)

Bases: _AbstractDiffusionTerm

Create a Term.

Parameters:

coeff (float or CellVariable or FaceVariable) – Coefficient for the term. FaceVariable objects are only acceptable for diffusion or convection terms.

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)
__neg__()
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

class fipy.DiffusionTermNoCorrection(coeff=(1.0,), var=None)

Bases: _AbstractDiffusionTerm

Create a Term.

Parameters:

coeff (float or CellVariable or FaceVariable) – Coefficient for the term. FaceVariable objects are only acceptable for diffusion or convection terms.

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)
__neg__()
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

class fipy.DistanceVariable(*args, **kwds)

Bases: CellVariable

A DistanceVariable object calculates \phi so it satisfies,

\abs{\nabla \phi} = 1

using the fast marching method with an initial condition defined by the zero level set. The solution can either be first or second order.

Here we will define a few test cases. Firstly a 1D test case

>>> from fipy.meshes import Grid1D
>>> from fipy.tools import serialComm
>>> mesh = Grid1D(dx = .5, nx = 8, communicator=serialComm)
>>> from .distanceVariable import DistanceVariable
>>> var = DistanceVariable(mesh = mesh, value = (-1., -1., -1., -1., 1., 1., 1., 1.))
>>> var.calcDistanceFunction() 
>>> answer = (-1.75, -1.25, -.75, -0.25, 0.25, 0.75, 1.25, 1.75)
>>> print(var.allclose(answer)) 
1

A 1D test case with very small dimensions.

>>> dx = 1e-10
>>> mesh = Grid1D(dx = dx, nx = 8, communicator=serialComm)
>>> var = DistanceVariable(mesh = mesh, value = (-1., -1., -1., -1., 1., 1., 1., 1.))
>>> var.calcDistanceFunction() 
>>> answer = numerix.arange(8) * dx - 3.5 * dx
>>> print(var.allclose(answer)) 
1

A 2D test case to test _calcTrialValue for a pathological case.

>>> dx = 1.
>>> dy = 2.
>>> from fipy.meshes import Grid2D
>>> mesh = Grid2D(dx = dx, dy = dy, nx = 2, ny = 3, communicator=serialComm)
>>> var = DistanceVariable(mesh = mesh, value = (-1., 1., 1., 1., -1., 1.))

>>> var.calcDistanceFunction() 
>>> vbl = -dx * dy / numerix.sqrt(dx**2 + dy**2) / 2.
>>> vbr = dx / 2
>>> vml = dy / 2.
>>> crossProd = dx * dy
>>> dsq = dx**2 + dy**2
>>> top = vbr * dx**2 + vml * dy**2
>>> sqrt = crossProd**2 *(dsq - (vbr - vml)**2)
>>> sqrt = numerix.sqrt(max(sqrt, 0))
>>> vmr = (top + sqrt) / dsq
>>> answer = (vbl, vbr, vml, vmr, vbl, vbr)
>>> print(var.allclose(answer)) 
1

The extendVariable method solves the following equation for a given extensionVariable.

\nabla u \cdot \nabla \phi = 0

using the fast marching method with an initial condition defined at the zero level set.

>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(dx = 1., dy = 1., nx = 2, ny = 2, communicator=serialComm)
>>> var = DistanceVariable(mesh = mesh, value = (-1., 1., 1., 1.))
>>> var.calcDistanceFunction() 
>>> extensionVar = CellVariable(mesh = mesh, value = (-1, .5, 2, -1))
>>> tmp = 1 / numerix.sqrt(2)
>>> print(var.allclose((-tmp / 2, 0.5, 0.5, 0.5 + tmp))) 
1
>>> var.extendVariable(extensionVar, order=1) 
>>> print(extensionVar.allclose((1.25, .5, 2, 1.25))) 
1
>>> mesh = Grid2D(dx = 1., dy = 1., nx = 3, ny = 3, communicator=serialComm)
>>> var = DistanceVariable(mesh = mesh, value = (-1., 1., 1.,
...                                               1., 1., 1.,
...                                               1., 1., 1.))
>>> var.calcDistanceFunction(order=1) 
>>> extensionVar = CellVariable(mesh = mesh, value = (-1., .5, -1.,
...                                                    2., -1., -1.,
...                                                   -1., -1., -1.))

>>> v1 = 0.5 + tmp
>>> v2 = 1.5
>>> tmp1 = (v1 + v2) / 2 + numerix.sqrt(2. - (v1 - v2)**2) / 2
>>> tmp2 = tmp1 + 1 / numerix.sqrt(2)
>>> print(var.allclose((-tmp / 2, 0.5, 1.5, 0.5, 0.5 + tmp,
...                      tmp1, 1.5, tmp1, tmp2))) 
1
>>> answer = (1.25, .5, .5, 2, 1.25, 0.9544, 2, 1.5456, 1.25)
>>> var.extendVariable(extensionVar, order=1) 
>>> print(extensionVar.allclose(answer, rtol = 1e-4)) 
1

Test case for a bug that occurs when initializing the distance variable at the interface. Currently it is assumed that adjacent cells that are opposite sign neighbors have perpendicular normal vectors. In fact the two closest cells could have opposite normals.

>>> mesh = Grid1D(dx = 1., nx = 3, communicator=serialComm)
>>> var = DistanceVariable(mesh = mesh, value = (-1., 1., -1.))
>>> var.calcDistanceFunction() 
>>> print(var.allclose((-0.5, 0.5, -0.5))) 
1

Testing second order. This example failed with Scikit-fmm.

>>> mesh = Grid2D(dx = 1., dy = 1., nx = 4, ny = 4, communicator=serialComm)
>>> var = DistanceVariable(mesh = mesh, value = (-1., -1., 1., 1.,
...                                               -1., -1., 1., 1.,
...                                               1., 1., 1., 1.,
...                                               1, 1, 1, 1))
>>> var.calcDistanceFunction(order=2) 
>>> answer = [-1.30473785, -0.5, 0.5, 1.49923009,
...           -0.5, -0.35355339, 0.5, 1.45118446,
...            0.5, 0.5, 0.97140452, 1.76215286,
...            1.49923009, 1.45118446, 1.76215286, 2.33721352]
>>> print(numerix.allclose(var, answer, rtol=1e-9)) 
True

** A test for a bug in both LSMLIB and Scikit-fmm **

The following test gives different result depending on whether LSMLIB or Scikit-fmm is used. There is a deeper problem that is related to this issue. When a value becomes “known” after previously being a “trial” value it updates its neighbors’ values. In a second order scheme the neighbors one step away also need to be updated (if the in between cell is “known” and the far cell is a “trial” cell), but are not in either package. By luck (due to trial values having the same value), the values calculated in Scikit-fmm for the following example are correct although an example that didn’t work for Scikit-fmm could also be constructed.

>>> mesh = Grid2D(dx = 1., dy = 1., nx = 4, ny = 4, communicator=serialComm)
>>> var = DistanceVariable(mesh = mesh, value = (-1., -1., -1., -1.,
...                                               1.,  1., -1., -1.,
...                                               1.,  1., -1., -1.,
...                                               1.,  1., -1., -1.))
>>> var.calcDistanceFunction(order=2) 
>>> var.calcDistanceFunction(order=2) 
>>> answer = [-0.5,        -0.58578644, -1.08578644, -1.85136395,
...            0.5,         0.29289322, -0.58578644, -1.54389939,
...            1.30473785,  0.5,        -0.5,        -1.5,
...            1.49547948,  0.5,        -0.5,        -1.5]

The 3rd and 7th element are different for LSMLIB. This is because the 15th element is not “known” when the “trial” value for the 7th element is calculated. Scikit-fmm calculates the values in a slightly different order so gets a seemingly better answer, but this is just chance.

>>> print(numerix.allclose(var, answer, rtol=1e-9)) 
True

Creates a distanceVariable object.

Parameters:

  • mesh (Mesh) – The mesh that defines the geometry of this variable.

  • name (str) – The name of the variable.

  • value (float or array_like) – The initial value.

  • unit (str or PhysicalUnit) – The physical units of the variable

  • hasOld (bool) – Whether the variable maintains an old value.

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__(points=None, order=0, nearestCellIDs=None)

Interpolates the CellVariable to a set of points using a method that has a memory requirement on the order of Ncells by Npoints in general, but uses only Ncells when the CellVariable’s mesh is a UniformGrid object.

Tests

>>> from fipy import *
>>> m = Grid2D(nx=3, ny=2)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.))))
[ 0.5  1.5  1.5]
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.)), order=1))
[ 0.25  1.1   1.2 ]
>>> m0 = Grid2D(nx=2, ny=2, dx=1., dy=1.)
>>> m1 = Grid2D(nx=4, ny=4, dx=.5, dy=.5)
>>> x, y = m0.cellCenters
>>> v0 = CellVariable(mesh=m0, value=x * y)
>>> print(v0(m1.cellCenters.globalValue))
[ 0.25  0.25  0.75  0.75  0.25  0.25  0.75  0.75  0.75  0.75  2.25  2.25
  0.75  0.75  2.25  2.25]
>>> print(v0(m1.cellCenters.globalValue, order=1))
[ 0.125  0.25   0.5    0.625  0.25   0.375  0.875  1.     0.5    0.875
  1.875  2.25   0.625  1.     2.25   2.625]
Parameters:

  • points (tuple or list of tuple) – A point or set of points in the format (X, Y, Z)

  • order ({0, 1}) – The order of interpolation, default is 0

  • nearestCellIDs (array_like) – Optional argument if user can calculate own nearest cell IDs array, shape should be same as points

__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the CellVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new CellVariable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
property arithmeticFaceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
cacheMe(recursive=False)
calcDistanceFunction(order=2)

Calculates the distanceVariable as a distance function.

Parameters:

order ({1, 2}) – The order of accuracy for the distance function calculation

property cellInterfaceAreas

Returns the length of the interface that crosses the cell

A simple 1D test:

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(dx = 1., nx = 4)
>>> distanceVariable = DistanceVariable(mesh = mesh,
...                                     value = (-1.5, -0.5, 0.5, 1.5))
>>> answer = CellVariable(mesh=mesh, value=(0, 0., 1., 0))
>>> print(numerix.allclose(distanceVariable.cellInterfaceAreas,
...                        answer))
True

A 2D test case:

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(dx = 1., dy = 1., nx = 3, ny = 3)
>>> distanceVariable = DistanceVariable(mesh = mesh,
...                                     value = (1.5, 0.5, 1.5,
...                                              0.5, -0.5, 0.5,
...                                              1.5, 0.5, 1.5))
>>> answer = CellVariable(mesh=mesh,
...                       value=(0, 1, 0, 1, 0, 1, 0, 1, 0))
>>> print(numerix.allclose(distanceVariable.cellInterfaceAreas, answer))
True

Another 2D test case:

>>> mesh = Grid2D(dx = .5, dy = .5, nx = 2, ny = 2)
>>> from fipy.variables.cellVariable import CellVariable
>>> distanceVariable = DistanceVariable(mesh = mesh,
...                                     value = (-0.5, 0.5, 0.5, 1.5))
>>> answer = CellVariable(mesh=mesh,
...                       value=(0, numerix.sqrt(2) / 4,  numerix.sqrt(2) / 4, 0))
>>> print(numerix.allclose(distanceVariable.cellInterfaceAreas,
...                        answer))
True

Test to check that the circumference of a circle is, in fact, 2\pi r.

>>> mesh = Grid2D(dx = 0.05, dy = 0.05, nx = 20, ny = 20)
>>> r = 0.25
>>> x, y = mesh.cellCenters
>>> rad = numerix.sqrt((x - .5)**2 + (y - .5)**2) - r
>>> distanceVariable = DistanceVariable(mesh = mesh, value = rad)
>>> print(numerix.allclose(distanceVariable.cellInterfaceAreas.sum(), 1.57984690073))
1
property cellVolumeAverage

Return the cell-volume-weighted average of the CellVariable:

<\phi>_\text{vol} = \frac{\sum_\text{cells} \phi_\text{cell} V_\text{cell}} {\sum_\text{cells} V_\text{cell}}

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx = 3, ny = 1, dx = .5, dy = .1)
>>> var = CellVariable(value = (1, 2, 6), mesh = mesh)
>>> print(var.cellVolumeAverage)
3.0
constrain(value, where=None)

Constrains the CellVariable to value at a location specified by where.

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> v.constrain(0., where=m.facesLeft)
>>> v.faceGrad.constrain([1.], where=m.facesRight)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]

Changing the constraint changes the dependencies

>>> v.constrain(1., where=m.facesLeft)
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can be Variable

>>> c = Variable(0.)
>>> v.constrain(c, where=m.facesLeft)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> c.value = 1.
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can have a Variable mask.

>>> v = CellVariable(mesh=m)
>>> mask = FaceVariable(mesh=m, value=m.facesLeft)
>>> v.constrain(1., where=mask)
>>> print(v.faceValue)
[ 1.  0.  0.  0.]
>>> mask[:] = mask | m.facesRight
>>> print(v.faceValue)
[ 1.  0.  0.  1.]
property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Make an duplicate of the Variable

>>> a = Variable(value=3)
>>> b = a.copy()
>>> b
Variable(value=array(3))

The duplicate will not reflect changes made to the original

>>> a.setValue(5)
>>> b
Variable(value=array(3))

Check that this works for arrays.

>>> a = Variable(value=numerix.array((0, 1, 2)))
>>> b = a.copy()
>>> b
Variable(value=array([0, 1, 2]))
>>> a[1] = 3
>>> b
Variable(value=array([0, 1, 2]))
dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

extendVariable(extensionVariable, order=2)

Calculates the extension of extensionVariable from the zero level set.

Parameters:

extensionVariable (CellVariable) – The variable to extend from the zero level set.

property faceGrad

Return \nabla \phi as a rank-1 FaceVariable using differencing for the normal direction(second-order gradient).

property faceGradAverage

Deprecated since version 3.3: use grad.arithmeticFaceValue() instead

Return \nabla \phi as a rank-1 FaceVariable using averaging for the normal direction(second-order gradient)

property faceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
property gaussGrad

Return \frac{1}{V_P} \sum_f \vec{n} \phi_f A_f as a rank-1 CellVariable (first-order gradient).

getLSMshape()
getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue

Concatenate and return values from all processors

When running on a single processor, the result is identical to value.

property grad

Return \nabla \phi as a rank-1 CellVariable (first-order gradient).

property harmonicFaceValue

Returns a FaceVariable whose value corresponds to the harmonic interpolation of the adjacent cells:

\phi_f = \frac{\phi_1 \phi_2}{(\phi_2 - \phi_1) \frac{d_{f2}}{d_{12}} + \phi_1}

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (0.5 / 1.) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (1.0 / 3.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (5.0 / 55.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property leastSquaresGrad

Return \nabla \phi, which is determined by solving for \nabla \phi in the following matrix equation,

\nabla \phi \cdot \sum_f d_{AP}^2 \vec{n}_{AP} \otimes \vec{n}_{AP} = \sum_f d_{AP}^2 \left( \vec{n} \cdot \nabla \phi \right)_{AP}

The matrix equation is derived by minimizing the following least squares sum,

F \left( \phi_x, \phi_y \right) = \sqrt{\sum_f \left( d_{AP} \vec{n}_{AP} \cdot \nabla \phi - d_{AP} \left( \vec{n}_{AP} \cdot \nabla \phi \right)_{AP} \right)^2 }

Tests

>>> from fipy import Grid2D
>>> m = Grid2D(nx=2, ny=2, dx=0.1, dy=2.0)
>>> print(numerix.allclose(CellVariable(mesh=m, value=(0, 1, 3, 6)).leastSquaresGrad.globalValue, \
...                                     [[8.0, 8.0, 24.0, 24.0],
...                                      [1.2, 2.0, 1.2, 2.0]]))
True

>>> from fipy import Grid1D
>>> print(numerix.allclose(CellVariable(mesh=Grid1D(dx=(2.0, 1.0, 0.5)),
...                                     value=(0, 1, 2)).leastSquaresGrad.globalValue, [[0.461538461538, 0.8, 1.2]]))
True
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property minmodFaceValue

Returns a FaceVariable with a value that is the minimum of the absolute values of the adjacent cells. If the values are of opposite sign then the result is zero:

\phi_f = \begin{cases} \phi_1& \text{when $|\phi_1| \le |\phi_2|$},\\ \phi_2& \text{when $|\phi_2| < |\phi_1|$},\\ 0 & \text{when $\phi1 \phi2 < 0$} \end{cases}

>>> from fipy import *
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(1, 2)).minmodFaceValue)
[1 1 2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, -2)).minmodFaceValue)
[-1 -1 -2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, 2)).minmodFaceValue)
[-1  0  2]
property name
property numericValue
property old

Return the values of the CellVariable from the previous solution sweep.

Combinations of CellVariable’s should also return old values.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 2)
>>> from fipy.variables.cellVariable import CellVariable
>>> var1 = CellVariable(mesh = mesh, value = (2, 3), hasOld = 1)
>>> var2 = CellVariable(mesh = mesh, value = (3, 4))
>>> v = var1 * var2
>>> print(v)
[ 6 12]
>>> var1.value = ((3, 2))
>>> print(v)
[9 8]
>>> print(v.old)
[ 6 12]

The following small test is to correct for a bug when the operator does not just use variables.

>>> v1 = var1 * 3
>>> print(v1)
[9 6]
>>> print(v1.old)
[6 9]
put(indices, value)
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> c = Constraint(0., where=m.facesLeft)
>>> v.constrain(c)
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> v.release(constraint=c)
>>> print(v.faceValue)
[ 0.5  1.   2.   2.5]
setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
updateOld()

Set the values of the previous solution sweep to the current values.

>>> from fipy import *
>>> v = CellVariable(mesh=Grid1D(), hasOld=False)
>>> v.updateOld()
Traceback (most recent call last):
   ...
AssertionError: The updateOld method requires the CellVariable to have an old value. Set hasOld to True when instantiating the CellVariable.
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
class fipy.DummySolver(*args, **kwargs)

Bases: PETScSolver

Solver that doesn’t do anything.

PETSc is intolerant of having zeros on the diagonal

Create a Solver object.

Parameters:

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon – Preconditioner to use. Not all solver suites support preconditioners.

__del__()
__enter__()
__exit__(exc_type, exc_value, traceback)
__repr__()

Return repr(self).

class fipy.DummyViewer(vars, title=None, **kwlimits)

Bases: AbstractViewer

Substitute viewer that doesn’t do anything

Create a AbstractViewer object.

Parameters:

  • vars (CellVariable or list) – the CellVariable objects to display.

  • title (str, optional) – displayed at the top of the Viewer window

  • xmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

property limits
plot(filename=None)

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

class fipy.ExplicitDiffusionTerm(coeff=(1.0,), var=None)

Bases: _AbstractDiffusionTerm

The discretization for the ExplicitDiffusionTerm is given by

\int_V \nabla \cdot (\Gamma\nabla\phi) dV \simeq \sum_f \Gamma_f \frac{\phi_A^\text{old}-\phi_P^\text{old}}{d_{AP}} A_f

where \phi_A^\text{old} and \phi_P^\text{old} are the old values of the variable. The term is added to the RHS vector and makes no contribution to the solution matrix.

Create a Term.

Parameters:

coeff (float or CellVariable or FaceVariable) – Coefficient for the term. FaceVariable objects are only acceptable for diffusion or convection terms.

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)
__neg__()
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

class fipy.ExplicitUpwindConvectionTerm(coeff=1.0, var=None)

Bases: _AbstractUpwindConvectionTerm

The discretization for this Term is given by

\int_V \nabla \cdot (\vec{u} \phi)\,dV \simeq \sum_{f} (\vec{n} \cdot \vec{u})_f \phi_f A_f

where \phi_f=\alpha_f \phi_P^\text{old} +(1-\alpha_f)\phi_A^\text{old} and \alpha_f is calculated using the upwind scheme. For further details see Numerical Schemes.

Create a _AbstractConvectionTerm object.

>>> from fipy import *
>>> m = Grid1D(nx = 2)
>>> cv = CellVariable(mesh = m)
>>> fv = FaceVariable(mesh = m)
>>> vcv = CellVariable(mesh=m, rank=1)
>>> vfv = FaceVariable(mesh=m, rank=1)
>>> __ConvectionTerm(coeff = cv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = fv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = vcv)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = vfv)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = (1,))
__ConvectionTerm(coeff=(1,))
>>> ExplicitUpwindConvectionTerm(coeff = (0,)).solve(var=cv, solver=DummySolver()) 
Traceback (most recent call last):
    ...
TransientTermError: The equation requires a TransientTerm with explicit convection.
>>> (TransientTerm(0.) - ExplicitUpwindConvectionTerm(coeff = (0,))).solve(var=cv, solver=DummySolver(), dt=1.)

>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = 1)).solve(var=cv, solver=DummySolver(), dt=1.) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> m2 = Grid2D(nx=2, ny=1)
>>> cv2 = CellVariable(mesh=m2)
>>> vcv2 = CellVariable(mesh=m2, rank=1)
>>> vfv2 = FaceVariable(mesh=m2, rank=1)
>>> __ConvectionTerm(coeff=vcv2)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> __ConvectionTerm(coeff=vfv2)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = ((0,), (0,)))).solve(var=cv2, solver=DummySolver(), dt=1.)
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = (0, 0))).solve(var=cv2, solver=DummySolver(), dt=1.)
Parameters:

coeff (The Term’s coefficient value.) –

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

exception fipy.ExplicitVariableError(s='Terms with explicit Variables cannot mix with Terms with implicit Variables.')

Bases: Exception

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class fipy.ExponentialConvectionTerm(coeff=1.0, var=None)

Bases: _AsymmetricConvectionTerm

The discretization for this Term is given by

\int_V \nabla \cdot (\vec{u} \phi)\,dV \simeq \sum_{f} (\vec{n} \cdot \vec{u})_f \phi_f A_f

where \phi_f=\alpha_f \phi_P +(1-\alpha_f)\phi_A and \alpha_f is calculated using the exponential scheme. For further details see Numerical Schemes.

Create a _AbstractConvectionTerm object.

>>> from fipy import *
>>> m = Grid1D(nx = 2)
>>> cv = CellVariable(mesh = m)
>>> fv = FaceVariable(mesh = m)
>>> vcv = CellVariable(mesh=m, rank=1)
>>> vfv = FaceVariable(mesh=m, rank=1)
>>> __ConvectionTerm(coeff = cv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = fv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = vcv)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = vfv)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = (1,))
__ConvectionTerm(coeff=(1,))
>>> ExplicitUpwindConvectionTerm(coeff = (0,)).solve(var=cv, solver=DummySolver()) 
Traceback (most recent call last):
    ...
TransientTermError: The equation requires a TransientTerm with explicit convection.
>>> (TransientTerm(0.) - ExplicitUpwindConvectionTerm(coeff = (0,))).solve(var=cv, solver=DummySolver(), dt=1.)

>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = 1)).solve(var=cv, solver=DummySolver(), dt=1.) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> m2 = Grid2D(nx=2, ny=1)
>>> cv2 = CellVariable(mesh=m2)
>>> vcv2 = CellVariable(mesh=m2, rank=1)
>>> vfv2 = FaceVariable(mesh=m2, rank=1)
>>> __ConvectionTerm(coeff=vcv2)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> __ConvectionTerm(coeff=vfv2)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = ((0,), (0,)))).solve(var=cv2, solver=DummySolver(), dt=1.)
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = (0, 0))).solve(var=cv2, solver=DummySolver(), dt=1.)
Parameters:

coeff (The Term’s coefficient value.) –

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

class fipy.ExponentialNoiseVariable(*args, **kwds)

Bases: NoiseVariable

Represents an exponential distribution of random numbers with the probability distribution

\mu^{-1} e^{-\frac{x}{\mu}}

with a mean parameter \mu.

Seed the random module for the sake of deterministic test results.

>>> from fipy import numerix
>>> numerix.random.seed(1)

We generate noise on a uniform Cartesian mesh

>>> from fipy.variables.variable import Variable
>>> mean = Variable()
>>> from fipy.meshes import Grid2D
>>> noise = ExponentialNoiseVariable(mesh = Grid2D(nx = 100, ny = 100), mean = mean)

We histogram the root-volume-weighted noise distribution

>>> from fipy.variables.histogramVariable import HistogramVariable
>>> histogram = HistogramVariable(distribution = noise, dx = 0.1, nx = 100)

and compare to a Gaussian distribution

>>> from fipy.variables.cellVariable import CellVariable
>>> expdist = CellVariable(mesh = histogram.mesh)
>>> x = histogram.mesh.cellCenters[0]

>>> if __name__ == '__main__':
...     from fipy import Viewer
...     viewer = Viewer(vars=noise, datamin=0, datamax=5)
...     histoplot = Viewer(vars=(histogram, expdist),
...                        datamin=0, datamax=1.5)

>>> from fipy.tools.numerix import arange, exp

>>> for mu in arange(0.5, 3, 0.5):
...     mean.value = (mu)
...     expdist.value = ((1/mean)*exp(-x/mean))
...     if __name__ == '__main__':
...         import sys
...         print("mean: %g" % mean, file=sys.stderr)
...         viewer.plot()
...         histoplot.plot()

>>> print(abs(noise.faceGrad.divergence.cellVolumeAverage) < 5e-15)
1
random values with an exponential distribution histogram of random values with an exponential distribution
Parameters:

  • mesh (Mesh) – The mesh on which to define the noise.

  • mean (float) – The mean of the distribution \mu.

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__(points=None, order=0, nearestCellIDs=None)

Interpolates the CellVariable to a set of points using a method that has a memory requirement on the order of Ncells by Npoints in general, but uses only Ncells when the CellVariable’s mesh is a UniformGrid object.

Tests

>>> from fipy import *
>>> m = Grid2D(nx=3, ny=2)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.))))
[ 0.5  1.5  1.5]
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.)), order=1))
[ 0.25  1.1   1.2 ]
>>> m0 = Grid2D(nx=2, ny=2, dx=1., dy=1.)
>>> m1 = Grid2D(nx=4, ny=4, dx=.5, dy=.5)
>>> x, y = m0.cellCenters
>>> v0 = CellVariable(mesh=m0, value=x * y)
>>> print(v0(m1.cellCenters.globalValue))
[ 0.25  0.25  0.75  0.75  0.25  0.25  0.75  0.75  0.75  0.75  2.25  2.25
  0.75  0.75  2.25  2.25]
>>> print(v0(m1.cellCenters.globalValue, order=1))
[ 0.125  0.25   0.5    0.625  0.25   0.375  0.875  1.     0.5    0.875
  1.875  2.25   0.625  1.     2.25   2.625]
Parameters:

  • points (tuple or list of tuple) – A point or set of points in the format (X, Y, Z)

  • order ({0, 1}) – The order of interpolation, default is 0

  • nearestCellIDs (array_like) – Optional argument if user can calculate own nearest cell IDs array, shape should be same as points

__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the CellVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new CellVariable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
property arithmeticFaceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
cacheMe(recursive=False)
property cellVolumeAverage

Return the cell-volume-weighted average of the CellVariable:

<\phi>_\text{vol} = \frac{\sum_\text{cells} \phi_\text{cell} V_\text{cell}} {\sum_\text{cells} V_\text{cell}}

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx = 3, ny = 1, dx = .5, dy = .1)
>>> var = CellVariable(value = (1, 2, 6), mesh = mesh)
>>> print(var.cellVolumeAverage)
3.0
constrain(value, where=None)

Constrains the CellVariable to value at a location specified by where.

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> v.constrain(0., where=m.facesLeft)
>>> v.faceGrad.constrain([1.], where=m.facesRight)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]

Changing the constraint changes the dependencies

>>> v.constrain(1., where=m.facesLeft)
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can be Variable

>>> c = Variable(0.)
>>> v.constrain(c, where=m.facesLeft)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> c.value = 1.
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can have a Variable mask.

>>> v = CellVariable(mesh=m)
>>> mask = FaceVariable(mesh=m, value=m.facesLeft)
>>> v.constrain(1., where=mask)
>>> print(v.faceValue)
[ 1.  0.  0.  0.]
>>> mask[:] = mask | m.facesRight
>>> print(v.faceValue)
[ 1.  0.  0.  1.]
property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Copy the value of the NoiseVariable to a static CellVariable.

dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

property faceGrad

Return \nabla \phi as a rank-1 FaceVariable using differencing for the normal direction(second-order gradient).

property faceGradAverage

Deprecated since version 3.3: use grad.arithmeticFaceValue() instead

Return \nabla \phi as a rank-1 FaceVariable using averaging for the normal direction(second-order gradient)

property faceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
property gaussGrad

Return \frac{1}{V_P} \sum_f \vec{n} \phi_f A_f as a rank-1 CellVariable (first-order gradient).

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue

Concatenate and return values from all processors

When running on a single processor, the result is identical to value.

property grad

Return \nabla \phi as a rank-1 CellVariable (first-order gradient).

property harmonicFaceValue

Returns a FaceVariable whose value corresponds to the harmonic interpolation of the adjacent cells:

\phi_f = \frac{\phi_1 \phi_2}{(\phi_2 - \phi_1) \frac{d_{f2}}{d_{12}} + \phi_1}

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (0.5 / 1.) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (1.0 / 3.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (5.0 / 55.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property leastSquaresGrad

Return \nabla \phi, which is determined by solving for \nabla \phi in the following matrix equation,

\nabla \phi \cdot \sum_f d_{AP}^2 \vec{n}_{AP} \otimes \vec{n}_{AP} = \sum_f d_{AP}^2 \left( \vec{n} \cdot \nabla \phi \right)_{AP}

The matrix equation is derived by minimizing the following least squares sum,

F \left( \phi_x, \phi_y \right) = \sqrt{\sum_f \left( d_{AP} \vec{n}_{AP} \cdot \nabla \phi - d_{AP} \left( \vec{n}_{AP} \cdot \nabla \phi \right)_{AP} \right)^2 }

Tests

>>> from fipy import Grid2D
>>> m = Grid2D(nx=2, ny=2, dx=0.1, dy=2.0)
>>> print(numerix.allclose(CellVariable(mesh=m, value=(0, 1, 3, 6)).leastSquaresGrad.globalValue, \
...                                     [[8.0, 8.0, 24.0, 24.0],
...                                      [1.2, 2.0, 1.2, 2.0]]))
True

>>> from fipy import Grid1D
>>> print(numerix.allclose(CellVariable(mesh=Grid1D(dx=(2.0, 1.0, 0.5)),
...                                     value=(0, 1, 2)).leastSquaresGrad.globalValue, [[0.461538461538, 0.8, 1.2]]))
True
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property minmodFaceValue

Returns a FaceVariable with a value that is the minimum of the absolute values of the adjacent cells. If the values are of opposite sign then the result is zero:

\phi_f = \begin{cases} \phi_1& \text{when $|\phi_1| \le |\phi_2|$},\\ \phi_2& \text{when $|\phi_2| < |\phi_1|$},\\ 0 & \text{when $\phi1 \phi2 < 0$} \end{cases}

>>> from fipy import *
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(1, 2)).minmodFaceValue)
[1 1 2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, -2)).minmodFaceValue)
[-1 -1 -2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, 2)).minmodFaceValue)
[-1  0  2]
property name
property numericValue
property old

Return the values of the CellVariable from the previous solution sweep.

Combinations of CellVariable’s should also return old values.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 2)
>>> from fipy.variables.cellVariable import CellVariable
>>> var1 = CellVariable(mesh = mesh, value = (2, 3), hasOld = 1)
>>> var2 = CellVariable(mesh = mesh, value = (3, 4))
>>> v = var1 * var2
>>> print(v)
[ 6 12]
>>> var1.value = ((3, 2))
>>> print(v)
[9 8]
>>> print(v.old)
[ 6 12]

The following small test is to correct for a bug when the operator does not just use variables.

>>> v1 = var1 * 3
>>> print(v1)
[9 6]
>>> print(v1.old)
[6 9]
parallelRandom()
put(indices, value)
random()
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> c = Constraint(0., where=m.facesLeft)
>>> v.constrain(c)
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> v.release(constraint=c)
>>> print(v.faceValue)
[ 0.5  1.   2.   2.5]
scramble()

Generate a new random distribution.

setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
updateOld()

Set the values of the previous solution sweep to the current values.

>>> from fipy import *
>>> v = CellVariable(mesh=Grid1D(), hasOld=False)
>>> v.updateOld()
Traceback (most recent call last):
   ...
AssertionError: The updateOld method requires the CellVariable to have an old value. Set hasOld to True when instantiating the CellVariable.
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
class fipy.FaceVariable(*args, **kwds)

Bases: _MeshVariable

Parameters:

  • mesh (Mesh) – the mesh that defines the geometry of this Variable

  • name (str) – the user-readable name of the Variable

  • value (float or array_like) – the initial value

  • rank (int) – the rank (number of dimensions) of each element of this Variable. Default: 0

  • elementshape (tuple of int) – the shape of each element of this variable Default: rank * (mesh.dim,)

  • unit (str or PhysicalUnit) – The physical units of the variable

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__()

“Evaluate” the Variable and return its value

>>> a = Variable(value=3)
>>> print(a())
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b()
7
__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the _MeshVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new Variable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
cacheMe(recursive=False)
constrain(value, where=None)

Constrain the Variable to have a value at an index or mask location specified by where.

>>> v = Variable((0, 1, 2, 3))
>>> v.constrain(2, numerix.array((True, False, False, False)))
>>> print(v)
[2 1 2 3]
>>> v[:] = 10
>>> print(v)
[ 2 10 10 10]
>>> v.constrain(5, numerix.array((False, False, True, False)))
>>> print(v)
[ 2 10  5 10]
>>> v[:] = 6
>>> print(v)
[2 6 5 6]
>>> v.constrain(8)
>>> print(v)
[8 8 8 8]
>>> v[:] = 10
>>> print(v)
[8 8 8 8]
>>> del v.constraints[2]
>>> print(v)
[ 2 10  5 10]

>>> from fipy.variables.cellVariable import CellVariable
>>> from fipy.meshes import Grid2D
>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v = CellVariable(mesh=m, rank=1, value=(x, y))
>>> v.constrain(((0.,), (-1.,)), where=m.facesLeft)
>>> print(v.faceValue)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.   1.   1.5  0.   1.   1.5]
 [ 0.5  0.5  1.   1.   1.5  1.5 -1.   0.5  0.5 -1.   1.5  1.5]]
Parameters:

  • value (float or array_like) – The value of the constraint

  • where (array_like of bool) – The constraint mask or index specifying the location of the constraint

property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Make an duplicate of the Variable

>>> a = Variable(value=3)
>>> b = a.copy()
>>> b
Variable(value=array(3))

The duplicate will not reflect changes made to the original

>>> a.setValue(5)
>>> b
Variable(value=array(3))

Check that this works for arrays.

>>> a = Variable(value=numerix.array((0, 1, 2)))
>>> b = a.copy()
>>> b
Variable(value=array([0, 1, 2]))
>>> a[1] = 3
>>> b
Variable(value=array([0, 1, 2]))
property divergence

the divergence of self, \vec{u},

\nabla\cdot\vec{u} \approx \frac{\sum_f (\vec{u}\cdot\hat{n})_f A_f}{V_P}

Returns:

divergence – one rank lower than self

Return type:

fipy.variables.cellVariable.CellVariable

Examples

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=3, ny=2)
>>> from builtins import range
>>> var = CellVariable(mesh=mesh, value=list(range(3*2)))
>>> print(var.faceGrad.divergence)
[ 4.  3.  2. -2. -3. -4.]
dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property name
property numericValue
put(indices, value)
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> v = Variable((0, 1, 2, 3))
>>> v.constrain(2, numerix.array((True, False, False, False)))
>>> v[:] = 10
>>> from fipy.boundaryConditions.constraint import Constraint
>>> c1 = Constraint(5, numerix.array((False, False, True, False)))
>>> v.constrain(c1)
>>> v[:] = 6
>>> v.constrain(8)
>>> v[:] = 10
>>> del v.constraints[2]
>>> v.release(constraint=c1)
>>> print(v)
[ 2 10 10 10]
setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
class fipy.FirstOrderAdvectionTerm(coeff=None)

Bases: _NonDiffusionTerm

The FirstOrderAdvectionTerm object constructs the b vector contribution for the advection term given by

u \abs{\nabla \phi}

from the advection equation given by:

\frac{\partial \phi}{\partial t} + u \abs{\nabla \phi} = 0

The construction of the gradient magnitude term requires upwinding. The formula used here is given by:

u_P \abs{\nabla \phi}_P = \max \left( u_P , 0 \right) \left[ \sum_A \min \left( \frac{ \phi_A - \phi_P } { d_{AP}}, 0 \right)^2 \right]^{1/2} + \min \left( u_P , 0 \right) \left[ \sum_A \max \left( \frac{ \phi_A - \phi_P } { d_{AP}}, 0 \right)^2 \right]^{1/2}

Here are some simple test cases for this problem:

>>> from fipy.meshes import Grid1D
>>> from fipy.solvers import *
>>> SparseMatrix = LinearLUSolver()._matrixClass
>>> mesh = Grid1D(dx = 1., nx = 3)
>>> from fipy.variables.cellVariable import CellVariable

Trivial test:

>>> var = CellVariable(value = numerix.zeros(3, 'd'), mesh = mesh)
>>> v, L, b = FirstOrderAdvectionTerm(0.)._buildMatrix(var, SparseMatrix)
>>> print(numerix.allclose(b, numerix.zeros(3, 'd'), atol = 1e-10)) 
True

Less trivial test:

>>> var = CellVariable(value = numerix.arange(3), mesh = mesh)
>>> v, L, b = FirstOrderAdvectionTerm(1.)._buildMatrix(var, SparseMatrix)
>>> print(numerix.allclose(b, numerix.array((0., -1., -1.)), atol = 1e-10)) 
True

Even less trivial

>>> var = CellVariable(value = numerix.arange(3), mesh = mesh)
>>> v, L, b = FirstOrderAdvectionTerm(-1.)._buildMatrix(var, SparseMatrix)
>>> print(numerix.allclose(b, numerix.array((1., 1., 0.)), atol = 1e-10)) 
True

Another trivial test case (more trivial than a trivial test case standing on a harpsichord singing “trivial test cases are here again”)

>>> vel = numerix.array((-1, 2, -3))
>>> var = CellVariable(value = numerix.array((4, 6, 1)), mesh = mesh)
>>> v, L, b = FirstOrderAdvectionTerm(vel)._buildMatrix(var, SparseMatrix)
>>> print(numerix.allclose(b, -vel * numerix.array((2, numerix.sqrt(5**2 + 2**2), 5)), atol = 1e-10)) 
True

Somewhat less trivial test case:

>>> from fipy.meshes import Grid2D
>>> mesh = Grid2D(dx = 1., dy = 1., nx = 2, ny = 2)
>>> vel = numerix.array((3, -5, -6, -3))
>>> var = CellVariable(value = numerix.array((3, 1, 6, 7)), mesh = mesh)
>>> v, L, b = FirstOrderAdvectionTerm(vel)._buildMatrix(var, SparseMatrix)
>>> answer = -vel * numerix.array((2, numerix.sqrt(2**2 + 6**2), 1, 0))
>>> print(numerix.allclose(b, answer, atol = 1e-10)) 
True

Create a Term.

Parameters:

coeff (float or CellVariable or FaceVariable) – Coefficient for the term. FaceVariable objects are only acceptable for diffusion or convection terms.

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

class fipy.FixedFlux(faces, value)

Bases: BoundaryCondition

The FixedFlux boundary condition adds a contribution, equivalent to a fixed flux (Neumann condition), to the equation’s RHS vector. The contribution, given by value, is only added to entries corresponding to the specified faces, and is weighted by the face areas.

Parameters:

  • faces (FaceVariable of bool) – Mask of faces where this condition applies.

  • value (float) – Value to impose.

__repr__()

Return repr(self).

class fipy.FixedValue(faces, value)

Bases: BoundaryCondition

The FixedValue boundary condition adds a contribution, equivalent to a fixed value (Dirichlet condition), to the equation’s RHS vector and coefficient matrix. The contributions are given by -\mathtt{value}\times G_{\text{face}} for the RHS vector and G_{\text{face}} for the coefficient matrix. The parameter G_{\text{face}} represents the term’s geometric coefficient, which depends on the type of term and the mesh geometry.

Contributions are only added to entries corresponding to the specified faces.

Parameters:

  • faces (FaceVariable of bool) – Mask of faces where this condition applies.

  • value (float) – Value to impose.

__repr__()

Return repr(self).

class fipy.GammaNoiseVariable(*args, **kwds)

Bases: NoiseVariable

Represents a gamma distribution of random numbers with the probability distribution

x^{\alpha - 1}\frac{\beta^\alpha e^{-\beta x}}{\Gamma(\alpha)}

with a shape parameter \alpha, a rate parameter \beta, and \Gamma(z) = \int_0^\infty t^{z - 1}e^{-t}\,dt.

Seed the random module for the sake of deterministic test results.

>>> from fipy import numerix
>>> numerix.random.seed(1)

We generate noise on a uniform Cartesian mesh

>>> from fipy.variables.variable import Variable
>>> alpha = Variable()
>>> beta = Variable()
>>> from fipy.meshes import Grid2D
>>> noise = GammaNoiseVariable(mesh = Grid2D(nx = 100, ny = 100), shape = alpha, rate = beta)

We histogram the root-volume-weighted noise distribution

>>> from fipy.variables.histogramVariable import HistogramVariable
>>> histogram = HistogramVariable(distribution = noise, dx = 0.1, nx = 300)

and compare to a Gaussian distribution

>>> from fipy.variables.cellVariable import CellVariable
>>> x = CellVariable(mesh=histogram.mesh, value=histogram.mesh.cellCenters[0])
>>> from scipy.special import gamma as Gamma 
>>> from fipy.tools.numerix import exp
>>> gammadist = (x**(alpha - 1) * (beta**alpha * exp(-beta * x)) / Gamma(alpha)) 

>>> if __name__ == '__main__':
...     from fipy import Viewer
...     viewer = Viewer(vars=noise, datamin=0, datamax=30)
...     histoplot = Viewer(vars=(histogram, gammadist),
...                        datamin=0, datamax=1)

>>> from fipy.tools.numerix import arange

>>> for shape in arange(1, 8, 1):
...     alpha.value = shape
...     for rate in arange(0.5, 2.5, 0.5):
...         beta.value = rate
...         if __name__ == '__main__':
...             import sys
...             print("alpha: %g, beta: %g" % (alpha, beta), file=sys.stderr)
...             viewer.plot()
...             histoplot.plot()

>>> print(abs(noise.faceGrad.divergence.cellVolumeAverage) < 5e-15)
1
random values with a gamma distribution histogram of random values with a gamma distribution
Parameters:

  • mesh (Mesh) – The mesh on which to define the noise.

  • shape (float) – The shape parameter, \alpha.

  • rate (float) – The rate or inverse scale parameter, \beta.

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__(points=None, order=0, nearestCellIDs=None)

Interpolates the CellVariable to a set of points using a method that has a memory requirement on the order of Ncells by Npoints in general, but uses only Ncells when the CellVariable’s mesh is a UniformGrid object.

Tests

>>> from fipy import *
>>> m = Grid2D(nx=3, ny=2)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.))))
[ 0.5  1.5  1.5]
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.)), order=1))
[ 0.25  1.1   1.2 ]
>>> m0 = Grid2D(nx=2, ny=2, dx=1., dy=1.)
>>> m1 = Grid2D(nx=4, ny=4, dx=.5, dy=.5)
>>> x, y = m0.cellCenters
>>> v0 = CellVariable(mesh=m0, value=x * y)
>>> print(v0(m1.cellCenters.globalValue))
[ 0.25  0.25  0.75  0.75  0.25  0.25  0.75  0.75  0.75  0.75  2.25  2.25
  0.75  0.75  2.25  2.25]
>>> print(v0(m1.cellCenters.globalValue, order=1))
[ 0.125  0.25   0.5    0.625  0.25   0.375  0.875  1.     0.5    0.875
  1.875  2.25   0.625  1.     2.25   2.625]
Parameters:

  • points (tuple or list of tuple) – A point or set of points in the format (X, Y, Z)

  • order ({0, 1}) – The order of interpolation, default is 0

  • nearestCellIDs (array_like) – Optional argument if user can calculate own nearest cell IDs array, shape should be same as points

__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the CellVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new CellVariable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
property arithmeticFaceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
cacheMe(recursive=False)
property cellVolumeAverage

Return the cell-volume-weighted average of the CellVariable:

<\phi>_\text{vol} = \frac{\sum_\text{cells} \phi_\text{cell} V_\text{cell}} {\sum_\text{cells} V_\text{cell}}

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx = 3, ny = 1, dx = .5, dy = .1)
>>> var = CellVariable(value = (1, 2, 6), mesh = mesh)
>>> print(var.cellVolumeAverage)
3.0
constrain(value, where=None)

Constrains the CellVariable to value at a location specified by where.

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> v.constrain(0., where=m.facesLeft)
>>> v.faceGrad.constrain([1.], where=m.facesRight)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]

Changing the constraint changes the dependencies

>>> v.constrain(1., where=m.facesLeft)
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can be Variable

>>> c = Variable(0.)
>>> v.constrain(c, where=m.facesLeft)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> c.value = 1.
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can have a Variable mask.

>>> v = CellVariable(mesh=m)
>>> mask = FaceVariable(mesh=m, value=m.facesLeft)
>>> v.constrain(1., where=mask)
>>> print(v.faceValue)
[ 1.  0.  0.  0.]
>>> mask[:] = mask | m.facesRight
>>> print(v.faceValue)
[ 1.  0.  0.  1.]
property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Copy the value of the NoiseVariable to a static CellVariable.

dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

property faceGrad

Return \nabla \phi as a rank-1 FaceVariable using differencing for the normal direction(second-order gradient).

property faceGradAverage

Deprecated since version 3.3: use grad.arithmeticFaceValue() instead

Return \nabla \phi as a rank-1 FaceVariable using averaging for the normal direction(second-order gradient)

property faceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
property gaussGrad

Return \frac{1}{V_P} \sum_f \vec{n} \phi_f A_f as a rank-1 CellVariable (first-order gradient).

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue

Concatenate and return values from all processors

When running on a single processor, the result is identical to value.

property grad

Return \nabla \phi as a rank-1 CellVariable (first-order gradient).

property harmonicFaceValue

Returns a FaceVariable whose value corresponds to the harmonic interpolation of the adjacent cells:

\phi_f = \frac{\phi_1 \phi_2}{(\phi_2 - \phi_1) \frac{d_{f2}}{d_{12}} + \phi_1}

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (0.5 / 1.) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (1.0 / 3.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (5.0 / 55.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property leastSquaresGrad

Return \nabla \phi, which is determined by solving for \nabla \phi in the following matrix equation,

\nabla \phi \cdot \sum_f d_{AP}^2 \vec{n}_{AP} \otimes \vec{n}_{AP} = \sum_f d_{AP}^2 \left( \vec{n} \cdot \nabla \phi \right)_{AP}

The matrix equation is derived by minimizing the following least squares sum,

F \left( \phi_x, \phi_y \right) = \sqrt{\sum_f \left( d_{AP} \vec{n}_{AP} \cdot \nabla \phi - d_{AP} \left( \vec{n}_{AP} \cdot \nabla \phi \right)_{AP} \right)^2 }

Tests

>>> from fipy import Grid2D
>>> m = Grid2D(nx=2, ny=2, dx=0.1, dy=2.0)
>>> print(numerix.allclose(CellVariable(mesh=m, value=(0, 1, 3, 6)).leastSquaresGrad.globalValue, \
...                                     [[8.0, 8.0, 24.0, 24.0],
...                                      [1.2, 2.0, 1.2, 2.0]]))
True

>>> from fipy import Grid1D
>>> print(numerix.allclose(CellVariable(mesh=Grid1D(dx=(2.0, 1.0, 0.5)),
...                                     value=(0, 1, 2)).leastSquaresGrad.globalValue, [[0.461538461538, 0.8, 1.2]]))
True
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property minmodFaceValue

Returns a FaceVariable with a value that is the minimum of the absolute values of the adjacent cells. If the values are of opposite sign then the result is zero:

\phi_f = \begin{cases} \phi_1& \text{when $|\phi_1| \le |\phi_2|$},\\ \phi_2& \text{when $|\phi_2| < |\phi_1|$},\\ 0 & \text{when $\phi1 \phi2 < 0$} \end{cases}

>>> from fipy import *
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(1, 2)).minmodFaceValue)
[1 1 2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, -2)).minmodFaceValue)
[-1 -1 -2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, 2)).minmodFaceValue)
[-1  0  2]
property name
property numericValue
property old

Return the values of the CellVariable from the previous solution sweep.

Combinations of CellVariable’s should also return old values.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 2)
>>> from fipy.variables.cellVariable import CellVariable
>>> var1 = CellVariable(mesh = mesh, value = (2, 3), hasOld = 1)
>>> var2 = CellVariable(mesh = mesh, value = (3, 4))
>>> v = var1 * var2
>>> print(v)
[ 6 12]
>>> var1.value = ((3, 2))
>>> print(v)
[9 8]
>>> print(v.old)
[ 6 12]

The following small test is to correct for a bug when the operator does not just use variables.

>>> v1 = var1 * 3
>>> print(v1)
[9 6]
>>> print(v1.old)
[6 9]
parallelRandom()
put(indices, value)
random()
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> c = Constraint(0., where=m.facesLeft)
>>> v.constrain(c)
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> v.release(constraint=c)
>>> print(v.faceValue)
[ 0.5  1.   2.   2.5]
scramble()

Generate a new random distribution.

setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
updateOld()

Set the values of the previous solution sweep to the current values.

>>> from fipy import *
>>> v = CellVariable(mesh=Grid1D(), hasOld=False)
>>> v.updateOld()
Traceback (most recent call last):
   ...
AssertionError: The updateOld method requires the CellVariable to have an old value. Set hasOld to True when instantiating the CellVariable.
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
class fipy.GaussianNoiseVariable(*args, **kwds)

Bases: NoiseVariable

Represents a normal (Gaussian) distribution of random numbers with mean \mu and variance \langle \eta(\vec{r}) \eta(\vec{r}\,') \rangle = \sigma^2, which has the probability distribution

\frac{1}{\sigma\sqrt{2\pi}} \exp -\frac{(x - \mu)^2}{2\sigma^2}

For example, the variance of thermal noise that is uncorrelated in space and time is often expressed as

\left\langle \eta(\vec{r}, t) \eta(\vec{r}\,', t') \right\rangle = M k_B T \delta(\vec{r} - \vec{r}\,')\delta(t - t')

which can be obtained with:

sigmaSqrd = Mobility * kBoltzmann * Temperature / (mesh.cellVolumes * timeStep)
GaussianNoiseVariable(mesh = mesh, variance = sigmaSqrd)

Note

If the time step will change as the simulation progresses, either through use of an adaptive iterator or by making manual changes at different stages, remember to declare timeStep as a Variable and to change its value with its setValue() method.

>>> import sys
>>> from fipy.tools.numerix import *

>>> mean = 0.
>>> variance = 4.

Seed the random module for the sake of deterministic test results.

>>> from fipy import numerix
>>> numerix.random.seed(3)

We generate noise on a non-uniform Cartesian mesh with cell dimensions of x^2 and y^3.

>>> from fipy.meshes import Grid2D
>>> mesh = Grid2D(dx = arange(0.1, 5., 0.1)**2, dy = arange(0.1, 3., 0.1)**3)
>>> from fipy.variables.cellVariable import CellVariable
>>> volumes = CellVariable(mesh=mesh, value=mesh.cellVolumes)
>>> noise = GaussianNoiseVariable(mesh = mesh, mean = mean,
...                               variance = variance / volumes)

We histogram the root-volume-weighted noise distribution

>>> from fipy.variables.histogramVariable import HistogramVariable
>>> histogram = HistogramVariable(distribution = noise * sqrt(volumes),
...                               dx = 0.1, nx = 600, offset = -30)

and compare to a Gaussian distribution

>>> gauss = CellVariable(mesh = histogram.mesh)
>>> x = histogram.mesh.cellCenters[0]
>>> gauss.value = ((1/(sqrt(variance * 2 * pi))) * exp(-(x - mean)**2 / (2 * variance)))

>>> if __name__ == '__main__':
...     from fipy.viewers import Viewer
...     viewer = Viewer(vars=noise,
...                     datamin=-5, datamax=5)
...     histoplot = Viewer(vars=(histogram, gauss))

>>> from builtins import range
>>> for i in range(10):
...     noise.scramble()
...     if __name__ == '__main__':
...         viewer.plot()
...         histoplot.plot()

>>> print(abs(noise.faceGrad.divergence.cellVolumeAverage) < 5e-15)
1

Note that the noise exhibits larger amplitude in the small cells than in the large ones

random values with a Gaussian distribution

but that the root-volume-weighted histogram is Gaussian.

histogram of random values with a Gaussian distribution
Parameters:

  • mesh (Mesh) – The mesh on which to define the noise.

  • mean (float) – The mean of the noise distribution, \mu.

  • variance (float) – The variance of the noise distribution, \sigma^2.

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__(points=None, order=0, nearestCellIDs=None)

Interpolates the CellVariable to a set of points using a method that has a memory requirement on the order of Ncells by Npoints in general, but uses only Ncells when the CellVariable’s mesh is a UniformGrid object.

Tests

>>> from fipy import *
>>> m = Grid2D(nx=3, ny=2)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.))))
[ 0.5  1.5  1.5]
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.)), order=1))
[ 0.25  1.1   1.2 ]
>>> m0 = Grid2D(nx=2, ny=2, dx=1., dy=1.)
>>> m1 = Grid2D(nx=4, ny=4, dx=.5, dy=.5)
>>> x, y = m0.cellCenters
>>> v0 = CellVariable(mesh=m0, value=x * y)
>>> print(v0(m1.cellCenters.globalValue))
[ 0.25  0.25  0.75  0.75  0.25  0.25  0.75  0.75  0.75  0.75  2.25  2.25
  0.75  0.75  2.25  2.25]
>>> print(v0(m1.cellCenters.globalValue, order=1))
[ 0.125  0.25   0.5    0.625  0.25   0.375  0.875  1.     0.5    0.875
  1.875  2.25   0.625  1.     2.25   2.625]
Parameters:

  • points (tuple or list of tuple) – A point or set of points in the format (X, Y, Z)

  • order ({0, 1}) – The order of interpolation, default is 0

  • nearestCellIDs (array_like) – Optional argument if user can calculate own nearest cell IDs array, shape should be same as points

__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the CellVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new CellVariable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
property arithmeticFaceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
cacheMe(recursive=False)
property cellVolumeAverage

Return the cell-volume-weighted average of the CellVariable:

<\phi>_\text{vol} = \frac{\sum_\text{cells} \phi_\text{cell} V_\text{cell}} {\sum_\text{cells} V_\text{cell}}

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx = 3, ny = 1, dx = .5, dy = .1)
>>> var = CellVariable(value = (1, 2, 6), mesh = mesh)
>>> print(var.cellVolumeAverage)
3.0
constrain(value, where=None)

Constrains the CellVariable to value at a location specified by where.

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> v.constrain(0., where=m.facesLeft)
>>> v.faceGrad.constrain([1.], where=m.facesRight)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]

Changing the constraint changes the dependencies

>>> v.constrain(1., where=m.facesLeft)
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can be Variable

>>> c = Variable(0.)
>>> v.constrain(c, where=m.facesLeft)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> c.value = 1.
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can have a Variable mask.

>>> v = CellVariable(mesh=m)
>>> mask = FaceVariable(mesh=m, value=m.facesLeft)
>>> v.constrain(1., where=mask)
>>> print(v.faceValue)
[ 1.  0.  0.  0.]
>>> mask[:] = mask | m.facesRight
>>> print(v.faceValue)
[ 1.  0.  0.  1.]
property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Copy the value of the NoiseVariable to a static CellVariable.

dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

property faceGrad

Return \nabla \phi as a rank-1 FaceVariable using differencing for the normal direction(second-order gradient).

property faceGradAverage

Deprecated since version 3.3: use grad.arithmeticFaceValue() instead

Return \nabla \phi as a rank-1 FaceVariable using averaging for the normal direction(second-order gradient)

property faceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
property gaussGrad

Return \frac{1}{V_P} \sum_f \vec{n} \phi_f A_f as a rank-1 CellVariable (first-order gradient).

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue

Concatenate and return values from all processors

When running on a single processor, the result is identical to value.

property grad

Return \nabla \phi as a rank-1 CellVariable (first-order gradient).

property harmonicFaceValue

Returns a FaceVariable whose value corresponds to the harmonic interpolation of the adjacent cells:

\phi_f = \frac{\phi_1 \phi_2}{(\phi_2 - \phi_1) \frac{d_{f2}}{d_{12}} + \phi_1}

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (0.5 / 1.) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (1.0 / 3.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (5.0 / 55.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property leastSquaresGrad

Return \nabla \phi, which is determined by solving for \nabla \phi in the following matrix equation,

\nabla \phi \cdot \sum_f d_{AP}^2 \vec{n}_{AP} \otimes \vec{n}_{AP} = \sum_f d_{AP}^2 \left( \vec{n} \cdot \nabla \phi \right)_{AP}

The matrix equation is derived by minimizing the following least squares sum,

F \left( \phi_x, \phi_y \right) = \sqrt{\sum_f \left( d_{AP} \vec{n}_{AP} \cdot \nabla \phi - d_{AP} \left( \vec{n}_{AP} \cdot \nabla \phi \right)_{AP} \right)^2 }

Tests

>>> from fipy import Grid2D
>>> m = Grid2D(nx=2, ny=2, dx=0.1, dy=2.0)
>>> print(numerix.allclose(CellVariable(mesh=m, value=(0, 1, 3, 6)).leastSquaresGrad.globalValue, \
...                                     [[8.0, 8.0, 24.0, 24.0],
...                                      [1.2, 2.0, 1.2, 2.0]]))
True

>>> from fipy import Grid1D
>>> print(numerix.allclose(CellVariable(mesh=Grid1D(dx=(2.0, 1.0, 0.5)),
...                                     value=(0, 1, 2)).leastSquaresGrad.globalValue, [[0.461538461538, 0.8, 1.2]]))
True
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property minmodFaceValue

Returns a FaceVariable with a value that is the minimum of the absolute values of the adjacent cells. If the values are of opposite sign then the result is zero:

\phi_f = \begin{cases} \phi_1& \text{when $|\phi_1| \le |\phi_2|$},\\ \phi_2& \text{when $|\phi_2| < |\phi_1|$},\\ 0 & \text{when $\phi1 \phi2 < 0$} \end{cases}

>>> from fipy import *
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(1, 2)).minmodFaceValue)
[1 1 2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, -2)).minmodFaceValue)
[-1 -1 -2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, 2)).minmodFaceValue)
[-1  0  2]
property name
property numericValue
property old

Return the values of the CellVariable from the previous solution sweep.

Combinations of CellVariable’s should also return old values.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 2)
>>> from fipy.variables.cellVariable import CellVariable
>>> var1 = CellVariable(mesh = mesh, value = (2, 3), hasOld = 1)
>>> var2 = CellVariable(mesh = mesh, value = (3, 4))
>>> v = var1 * var2
>>> print(v)
[ 6 12]
>>> var1.value = ((3, 2))
>>> print(v)
[9 8]
>>> print(v.old)
[ 6 12]

The following small test is to correct for a bug when the operator does not just use variables.

>>> v1 = var1 * 3
>>> print(v1)
[9 6]
>>> print(v1.old)
[6 9]
parallelRandom()
put(indices, value)
random()
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> c = Constraint(0., where=m.facesLeft)
>>> v.constrain(c)
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> v.release(constraint=c)
>>> print(v.faceValue)
[ 0.5  1.   2.   2.5]
scramble()

Generate a new random distribution.

setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
updateOld()

Set the values of the previous solution sweep to the current values.

>>> from fipy import *
>>> v = CellVariable(mesh=Grid1D(), hasOld=False)
>>> v.updateOld()
Traceback (most recent call last):
   ...
AssertionError: The updateOld method requires the CellVariable to have an old value. Set hasOld to True when instantiating the CellVariable.
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
fipy.GeneralSolver

alias of LinearGMRESSolver

class fipy.Gmsh2D(arg, coordDimensions=2, communicator=SerialPETScCommWrapper(), overlap=1, background=None)

Bases: Mesh2D

Construct a 2D Mesh using Gmsh

If called in parallel, the mesh will be partitioned based on the value of parallelComm.Nproc. If an MSH file is supplied, it must have been previously partitioned with the number of partitions matching parallelComm.Nproc.

>>> radius = 5.
>>> side = 4.
>>> squaredCircle = Gmsh2D('''
... // A mesh consisting of a square inside a circle inside a circle
...
... // define the basic dimensions of the mesh
...
... cellSize = 1;
... radius = %(radius)g;
... side = %(side)g;
...
... // define the compass points of the inner circle
...
... Point(1) = {0, 0, 0, cellSize};
... Point(2) = {-radius, 0, 0, cellSize};
... Point(3) = {0, radius, 0, cellSize};
... Point(4) = {radius, 0, 0, cellSize};
... Point(5) = {0, -radius, 0, cellSize};
...
... // define the compass points of the outer circle
...
... Point(6) = {-2*radius, 0, 0, cellSize};
... Point(7) = {0, 2*radius, 0, cellSize};
... Point(8) = {2*radius, 0, 0, cellSize};
... Point(9) = {0, -2*radius, 0, cellSize};
...
... // define the corners of the square
...
... Point(10) = {side/2, side/2, 0, cellSize/2};
... Point(11) = {-side/2, side/2, 0, cellSize/2};
... Point(12) = {-side/2, -side/2, 0, cellSize/2};
... Point(13) = {side/2, -side/2, 0, cellSize/2};
...
... // define the inner circle
...
... Circle(1) = {2, 1, 3};
... Circle(2) = {3, 1, 4};
... Circle(3) = {4, 1, 5};
... Circle(4) = {5, 1, 2};
...
... // define the outer circle
...
... Circle(5) = {6, 1, 7};
... Circle(6) = {7, 1, 8};
... Circle(7) = {8, 1, 9};
... Circle(8) = {9, 1, 6};
...
... // define the square
...
... Line(9) = {10, 13};
... Line(10) = {13, 12};
... Line(11) = {12, 11};
... Line(12) = {11, 10};
...
... // define the three boundaries
...
... Line Loop(1) = {1, 2, 3, 4};
... Line Loop(2) = {5, 6, 7, 8};
... Line Loop(3) = {9, 10, 11, 12};
...
... // define the three domains
...
... Plane Surface(1) = {2, 1};
... Plane Surface(2) = {1, 3};
... Plane Surface(3) = {3};
...
... // label the three domains
...
... // attention: if you use any "Physical" labels, you *must* label
... // all elements that correspond to FiPy Cells (Physical Surface in 2D
... // and Physical Volume in 3D) or Gmsh will not include them and FiPy
... // will not be able to include them in the Mesh.
...
... // note: if you do not use any labels, all Cells will be included.
...
... Physical Surface("Outer") = {1};
... Physical Surface("Middle") = {2};
... Physical Surface("Inner") = {3};
...
... // label the "north-west" part of the exterior boundary
...
... // note: you only need to label the Face elements
... // (Physical Line in 2D and Physical Surface in 3D) that correspond
... // to boundaries you are interested in. FiPy does not need them to
... // construct the Mesh.
...
... Physical Line("NW") = {5};
... ''' % locals())

It can be easier to specify certain domains and boundaries within Gmsh than it is to define the same domains and boundaries with FiPy expressions.

Here we compare obtaining the same Cells and Faces using FiPy’s parametric descriptions and Gmsh’s labels.

>>> x, y = squaredCircle.cellCenters 

>>> middle = ((x**2 + y**2 <= radius**2)
...           & ~((x > -side/2) & (x < side/2)
...               & (y > -side/2) & (y < side/2))) 

>>> print((middle == squaredCircle.physicalCells["Middle"]).all()) 
True

>>> X, Y = squaredCircle.faceCenters 

>>> NW = ((X**2 + Y**2 > (1.99*radius)**2)
...       & (X**2 + Y**2 < (2.01*radius)**2)
...       & (X <= 0) & (Y >= 0)) 

>>> print((NW == squaredCircle.physicalFaces["NW"]).all()) 
True

It is possible to direct Gmsh to give the mesh different densities in different locations

>>> geo = '''
... // A mesh consisting of a square
...
... // define the corners of the square
...
... Point(1) = {1, 1, 0, 1};
... Point(2) = {0, 1, 0, 1};
... Point(3) = {0, 0, 0, 1};
... Point(4) = {1, 0, 0, 1};
...
... // define the square
...
... Line(1) = {1, 2};
... Line(2) = {2, 3};
... Line(3) = {3, 4};
... Line(4) = {4, 1};
...
... // define the boundary
...
... Line Loop(1) = {1, 2, 3, 4};
...
... // define the domain
...
... Plane Surface(1) = {1};
... '''

>>> from fipy import CellVariable, numerix

>>> error = []
>>> bkg = None
>>> from builtins import range
>>> for refine in range(4):
...     square = Gmsh2D(geo, background=bkg) 
...     x, y = square.cellCenters 
...     bkg = CellVariable(mesh=square, value=abs(x / 4) + 0.01) 
...     error.append(((2 * numerix.sqrt(square.cellVolumes) / bkg - 1)**2).cellVolumeAverage)

Check that the mesh is (semi)monotonically approaching the desired density (the first step may increase, depending on the number of partitions)

>>> print(numerix.greater(error[:-1], error[1:]).all()) 
True

and that the final density is close enough to the desired density

>>> print(error[-1] < 0.02) 
True

The initial mesh doesn’t have to be from Gmsh

>>> from fipy import Tri2D

>>> trisquare = Tri2D(nx=1, ny=1)
>>> x, y = trisquare.cellCenters
>>> bkg = CellVariable(mesh=trisquare, value=abs(x / 4) + 0.01)
>>> std1 = (numerix.sqrt(2 * trisquare.cellVolumes) / bkg).std()

>>> square = Gmsh2D(geo, background=bkg) 
>>> x, y = square.cellCenters 
>>> bkg = CellVariable(mesh=square, value=abs(x / 4) + 0.01) 
>>> std2 = (numerix.sqrt(2 * square.cellVolumes) / bkg).std() 

>>> print(std1 > std2) 
True
Parameters:

  • arg (str) – (i) the path to an MSH file, (ii) a path to a Gmsh geometry (.geo) file, or (iii) a Gmsh geometry script

  • coordDimensions (int) – Dimension of shapes

  • overlap (int) – The number of overlapping cells for parallel simulations. Generally 1 is adequate. Higher order equations or discretizations require more. If overlap is greater than one, communication reverts to serial, as Gmsh only provides one layer of ghost cells.

  • background (CellVariable) – Specifies the desired characteristic lengths of the mesh cells

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
extrude(extrudeFunc=<function Mesh2D.<lambda>>, layers=1)

This function returns a new 3D mesh. The 2D mesh is extruded using the extrudeFunc and the number of layers.

>>> from fipy.meshes.nonUniformGrid2D import NonUniformGrid2D
>>> print(NonUniformGrid2D(nx=2, ny=2).extrude(layers=2).cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]]

>>> from fipy.meshes.tri2D import Tri2D
>>> print(Tri2D().extrude(layers=2).cellCenters.allclose([[ 0.83333333, 0.5,        0.16666667, 0.5,       0.83333333, 0.5,
...                                                      0.16666667, 0.5       ],
...                                                       [ 0.5,        0.83333333, 0.5,        0.16666667, 0.5,        0.83333333,
...                                                      0.5,        0.16666667],
...                                                      [ 0.5,        0.5,        0.5,        0.5,        1.5,        1.5,        1.5,
...                                                      1.5       ]]))
True
Parameters:

  • extrudeFunc (function) – Takes the vertex coordinates and returns the displaced values

  • layers (int) – Number of layers in the extruded mesh (number of times extrudeFunc will be called)

property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.Gmsh2DIn3DSpace(arg, communicator=SerialPETScCommWrapper(), overlap=1, background=None)

Bases: Gmsh2D

Create a topologically 2D Mesh in 3D coordinates using Gmsh

If called in parallel, the mesh will be partitioned based on the value of parallelComm.Nproc. If an MSH file is supplied, it must have been previously partitioned with the number of partitions matching parallelComm.Nproc.

Parameters:

  • arg (str) – (i) the path to an MSH file, (ii) a path to a Gmsh geometry (.geo) file, or (iii) a Gmsh geometry script

  • coordDimensions (int) – Dimension of shapes

  • overlap (int) – The number of overlapping cells for parallel simulations. Generally 1 is adequate. Higher order equations or discretizations require more. If overlap is greater than one, communication reverts to serial, as Gmsh only provides one layer of ghost cells.

  • background (CellVariable) – Specifies the desired characteristic lengths of the mesh cells

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
extrude(extrudeFunc=<function Mesh2D.<lambda>>, layers=1)

This function returns a new 3D mesh. The 2D mesh is extruded using the extrudeFunc and the number of layers.

>>> from fipy.meshes.nonUniformGrid2D import NonUniformGrid2D
>>> print(NonUniformGrid2D(nx=2, ny=2).extrude(layers=2).cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]]

>>> from fipy.meshes.tri2D import Tri2D
>>> print(Tri2D().extrude(layers=2).cellCenters.allclose([[ 0.83333333, 0.5,        0.16666667, 0.5,       0.83333333, 0.5,
...                                                      0.16666667, 0.5       ],
...                                                       [ 0.5,        0.83333333, 0.5,        0.16666667, 0.5,        0.83333333,
...                                                      0.5,        0.16666667],
...                                                      [ 0.5,        0.5,        0.5,        0.5,        1.5,        1.5,        1.5,
...                                                      1.5       ]]))
True
Parameters:

  • extrudeFunc (function) – Takes the vertex coordinates and returns the displaced values

  • layers (int) – Number of layers in the extruded mesh (number of times extrudeFunc will be called)

property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.Gmsh3D(arg, communicator=SerialPETScCommWrapper(), overlap=1, background=None)

Bases: Mesh

Create a 3D Mesh using Gmsh

If called in parallel, the mesh will be partitioned based on the value of parallelComm.Nproc. If an MSH file is supplied, it must have been previously partitioned with the number of partitions matching parallelComm.Nproc.

Parameters:

  • arg (str) – (i) the path to an MSH file, (ii) a path to a Gmsh geometry (.geo) file, or (iii) a Gmsh geometry script

  • overlap (int) – The number of overlapping cells for parallel simulations. Generally 1 is adequate. Higher order equations or discretizations require more. If overlap is greater than one, communication reverts to serial, as Gmsh only provides one layer of ghost cells.

  • background (CellVariable) – Specifies the desired characteristic lengths of the mesh cells

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.GmshGrid2D(dx=1.0, dy=1.0, nx=1, ny=None, coordDimensions=2, communicator=SerialPETScCommWrapper(), overlap=1)

Bases: Gmsh2D

Should serve as a drop-in replacement for Grid2D

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
extrude(extrudeFunc=<function Mesh2D.<lambda>>, layers=1)

This function returns a new 3D mesh. The 2D mesh is extruded using the extrudeFunc and the number of layers.

>>> from fipy.meshes.nonUniformGrid2D import NonUniformGrid2D
>>> print(NonUniformGrid2D(nx=2, ny=2).extrude(layers=2).cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]]

>>> from fipy.meshes.tri2D import Tri2D
>>> print(Tri2D().extrude(layers=2).cellCenters.allclose([[ 0.83333333, 0.5,        0.16666667, 0.5,       0.83333333, 0.5,
...                                                      0.16666667, 0.5       ],
...                                                       [ 0.5,        0.83333333, 0.5,        0.16666667, 0.5,        0.83333333,
...                                                      0.5,        0.16666667],
...                                                      [ 0.5,        0.5,        0.5,        0.5,        1.5,        1.5,        1.5,
...                                                      1.5       ]]))
True
Parameters:

  • extrudeFunc (function) – Takes the vertex coordinates and returns the displaced values

  • layers (int) – Number of layers in the extruded mesh (number of times extrudeFunc will be called)

property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.GmshGrid3D(dx=1.0, dy=1.0, dz=1.0, nx=1, ny=None, nz=None, communicator=SerialPETScCommWrapper(), overlap=1)

Bases: Gmsh3D

Should serve as a drop-in replacement for Grid3D

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
fipy.Grid1D(dx=1.0, nx=None, Lx=None, overlap=2, communicator=SerialPETScCommWrapper())

Create a 1D Cartesian mesh

Factory function to select between UniformGrid1D and NonUniformGrid1D. If Lx is specified the length of the domain is always Lx regardless of dx, unless dx is a list of spacings, in which case Lx will be the sum of dx and nx will be the count of dx.

Parameters:

  • dx (float) – Grid spacing in the horizontal direction

  • nx (int) – Number of cells in the horizontal direction

  • Lx (float) – Domain length in the horizontal direction

  • overlap (int) – Number of overlapping cells for parallel simulations. Generally 2 is adequate. Higher order equations or discretizations require more.

  • communicator (CommWrapper) – MPI communicator to use. Select serialComm to create a serial mesh when running in parallel; mostly used for test purposes. (default: parallelComm).

fipy.Grid2D(dx=1.0, dy=1.0, nx=None, ny=None, Lx=None, Ly=None, overlap=2, communicator=SerialPETScCommWrapper())

Create a 2D Cartesian mesh

Factory function to select between UniformGrid2D and NonUniformGrid2D. If L{x,y} is specified, the length of the domain is always L{x,y} regardless of d{x,y}, unless d{x,y} is a list of spacings, in which case L{x,y} will be the sum of d{x,y} and n{x,y} will be the count of d{x,y}.

>>> print(Grid2D(Lx=3., nx=2).dx)
1.5
Parameters:

  • dx (float) – Grid spacing in the horizontal direction

  • dy (float) – Grid spacing in the vertical direction

  • nx (int) – Number of cells in the horizontal direction

  • ny (int) – Number of cells in the vertical direction

  • Lx (float) – Domain length in the horizontal direction

  • Ly (float) – Domain length in the vertical direction

  • overlap (int) – Number of overlapping cells for parallel simulations. Generally 2 is adequate. Higher order equations or discretizations require more.

  • communicator (CommWrapper) – MPI communicator to use. Select serialComm to create a serial mesh when running in parallel; mostly used for test purposes. (default: parallelComm).

fipy.Grid3D(dx=1.0, dy=1.0, dz=1.0, nx=None, ny=None, nz=None, Lx=None, Ly=None, Lz=None, overlap=2, communicator=SerialPETScCommWrapper())

Create a 3D Cartesian mesh

Factory function to select between UniformGrid3D and NonUniformGrid3D. If L{x,y,z} is specified, the length of the domain is always L{x,y,z} regardless of d{x,y,z}, unless d{x,y,z} is a list of spacings, in which case L{x,y,z} will be the sum of d{x,y,z} and n{x,y,z} will be the count of d{x,y,z}.

Parameters:

  • dx (float) – Grid spacing in the horizontal direction

  • dy (float) – Grid spacing in the vertical direction

  • dz (float) – Grid spacing in the depth direction

  • nx (int) – Number of cells in the horizontal direction

  • ny (int) – Number of cells in the vertical direction

  • nz (int) – Number of cells in the depth direction

  • Lx (float) – Domain length in the horizontal direction

  • Ly (float) – Domain length in the vertical direction

  • Lz (float) – Domain length in the depth direction

  • overlap (int) – Number of overlapping cells for parallel simulations. Generally 2 is adequate. Higher order equations or discretizations require more.

  • communicator (CommWrapper) – MPI communicator to use. Select serialComm to create a serial mesh when running in parallel; mostly used for test purposes. (default: parallelComm).

class fipy.HistogramVariable(*args, **kwds)

Bases: CellVariable

Produces a histogram of the values of the supplied distribution.

Parameters:

  • distribution (array_like or Variable) – The collection of values to sample.

  • dx (float) – The bin size

  • nx (int) – The number of bins

  • offset (float) – The position of the first bin

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__(points=None, order=0, nearestCellIDs=None)

Interpolates the CellVariable to a set of points using a method that has a memory requirement on the order of Ncells by Npoints in general, but uses only Ncells when the CellVariable’s mesh is a UniformGrid object.

Tests

>>> from fipy import *
>>> m = Grid2D(nx=3, ny=2)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.))))
[ 0.5  1.5  1.5]
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.)), order=1))
[ 0.25  1.1   1.2 ]
>>> m0 = Grid2D(nx=2, ny=2, dx=1., dy=1.)
>>> m1 = Grid2D(nx=4, ny=4, dx=.5, dy=.5)
>>> x, y = m0.cellCenters
>>> v0 = CellVariable(mesh=m0, value=x * y)
>>> print(v0(m1.cellCenters.globalValue))
[ 0.25  0.25  0.75  0.75  0.25  0.25  0.75  0.75  0.75  0.75  2.25  2.25
  0.75  0.75  2.25  2.25]
>>> print(v0(m1.cellCenters.globalValue, order=1))
[ 0.125  0.25   0.5    0.625  0.25   0.375  0.875  1.     0.5    0.875
  1.875  2.25   0.625  1.     2.25   2.625]
Parameters:

  • points (tuple or list of tuple) – A point or set of points in the format (X, Y, Z)

  • order ({0, 1}) – The order of interpolation, default is 0

  • nearestCellIDs (array_like) – Optional argument if user can calculate own nearest cell IDs array, shape should be same as points

__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the CellVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new CellVariable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
property arithmeticFaceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
cacheMe(recursive=False)
property cellVolumeAverage

Return the cell-volume-weighted average of the CellVariable:

<\phi>_\text{vol} = \frac{\sum_\text{cells} \phi_\text{cell} V_\text{cell}} {\sum_\text{cells} V_\text{cell}}

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx = 3, ny = 1, dx = .5, dy = .1)
>>> var = CellVariable(value = (1, 2, 6), mesh = mesh)
>>> print(var.cellVolumeAverage)
3.0
constrain(value, where=None)

Constrains the CellVariable to value at a location specified by where.

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> v.constrain(0., where=m.facesLeft)
>>> v.faceGrad.constrain([1.], where=m.facesRight)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]

Changing the constraint changes the dependencies

>>> v.constrain(1., where=m.facesLeft)
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can be Variable

>>> c = Variable(0.)
>>> v.constrain(c, where=m.facesLeft)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> c.value = 1.
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can have a Variable mask.

>>> v = CellVariable(mesh=m)
>>> mask = FaceVariable(mesh=m, value=m.facesLeft)
>>> v.constrain(1., where=mask)
>>> print(v.faceValue)
[ 1.  0.  0.  0.]
>>> mask[:] = mask | m.facesRight
>>> print(v.faceValue)
[ 1.  0.  0.  1.]
property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Make an duplicate of the Variable

>>> a = Variable(value=3)
>>> b = a.copy()
>>> b
Variable(value=array(3))

The duplicate will not reflect changes made to the original

>>> a.setValue(5)
>>> b
Variable(value=array(3))

Check that this works for arrays.

>>> a = Variable(value=numerix.array((0, 1, 2)))
>>> b = a.copy()
>>> b
Variable(value=array([0, 1, 2]))
>>> a[1] = 3
>>> b
Variable(value=array([0, 1, 2]))
dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

property faceGrad

Return \nabla \phi as a rank-1 FaceVariable using differencing for the normal direction(second-order gradient).

property faceGradAverage

Deprecated since version 3.3: use grad.arithmeticFaceValue() instead

Return \nabla \phi as a rank-1 FaceVariable using averaging for the normal direction(second-order gradient)

property faceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
property gaussGrad

Return \frac{1}{V_P} \sum_f \vec{n} \phi_f A_f as a rank-1 CellVariable (first-order gradient).

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue

Concatenate and return values from all processors

When running on a single processor, the result is identical to value.

property grad

Return \nabla \phi as a rank-1 CellVariable (first-order gradient).

property harmonicFaceValue

Returns a FaceVariable whose value corresponds to the harmonic interpolation of the adjacent cells:

\phi_f = \frac{\phi_1 \phi_2}{(\phi_2 - \phi_1) \frac{d_{f2}}{d_{12}} + \phi_1}

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (0.5 / 1.) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (1.0 / 3.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (5.0 / 55.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property leastSquaresGrad

Return \nabla \phi, which is determined by solving for \nabla \phi in the following matrix equation,

\nabla \phi \cdot \sum_f d_{AP}^2 \vec{n}_{AP} \otimes \vec{n}_{AP} = \sum_f d_{AP}^2 \left( \vec{n} \cdot \nabla \phi \right)_{AP}

The matrix equation is derived by minimizing the following least squares sum,

F \left( \phi_x, \phi_y \right) = \sqrt{\sum_f \left( d_{AP} \vec{n}_{AP} \cdot \nabla \phi - d_{AP} \left( \vec{n}_{AP} \cdot \nabla \phi \right)_{AP} \right)^2 }

Tests

>>> from fipy import Grid2D
>>> m = Grid2D(nx=2, ny=2, dx=0.1, dy=2.0)
>>> print(numerix.allclose(CellVariable(mesh=m, value=(0, 1, 3, 6)).leastSquaresGrad.globalValue, \
...                                     [[8.0, 8.0, 24.0, 24.0],
...                                      [1.2, 2.0, 1.2, 2.0]]))
True

>>> from fipy import Grid1D
>>> print(numerix.allclose(CellVariable(mesh=Grid1D(dx=(2.0, 1.0, 0.5)),
...                                     value=(0, 1, 2)).leastSquaresGrad.globalValue, [[0.461538461538, 0.8, 1.2]]))
True
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property minmodFaceValue

Returns a FaceVariable with a value that is the minimum of the absolute values of the adjacent cells. If the values are of opposite sign then the result is zero:

\phi_f = \begin{cases} \phi_1& \text{when $|\phi_1| \le |\phi_2|$},\\ \phi_2& \text{when $|\phi_2| < |\phi_1|$},\\ 0 & \text{when $\phi1 \phi2 < 0$} \end{cases}

>>> from fipy import *
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(1, 2)).minmodFaceValue)
[1 1 2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, -2)).minmodFaceValue)
[-1 -1 -2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, 2)).minmodFaceValue)
[-1  0  2]
property name
property numericValue
property old

Return the values of the CellVariable from the previous solution sweep.

Combinations of CellVariable’s should also return old values.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 2)
>>> from fipy.variables.cellVariable import CellVariable
>>> var1 = CellVariable(mesh = mesh, value = (2, 3), hasOld = 1)
>>> var2 = CellVariable(mesh = mesh, value = (3, 4))
>>> v = var1 * var2
>>> print(v)
[ 6 12]
>>> var1.value = ((3, 2))
>>> print(v)
[9 8]
>>> print(v.old)
[ 6 12]

The following small test is to correct for a bug when the operator does not just use variables.

>>> v1 = var1 * 3
>>> print(v1)
[9 6]
>>> print(v1.old)
[6 9]
put(indices, value)
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> c = Constraint(0., where=m.facesLeft)
>>> v.constrain(c)
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> v.release(constraint=c)
>>> print(v.faceValue)
[ 0.5  1.   2.   2.5]
setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
updateOld()

Set the values of the previous solution sweep to the current values.

>>> from fipy import *
>>> v = CellVariable(mesh=Grid1D(), hasOld=False)
>>> v.updateOld()
Traceback (most recent call last):
   ...
AssertionError: The updateOld method requires the CellVariable to have an old value. Set hasOld to True when instantiating the CellVariable.
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
class fipy.HybridConvectionTerm(coeff=1.0, var=None)

Bases: _AsymmetricConvectionTerm

The discretization for this Term is given by

\int_V \nabla \cdot (\vec{u} \phi)\,dV \simeq \sum_{f} (\vec{n} \cdot \vec{u})_f \phi_f A_f

where \phi_f=\alpha_f \phi_P +(1-\alpha_f)\phi_A and \alpha_f is calculated using the hybrid scheme. For further details see Numerical Schemes.

Create a _AbstractConvectionTerm object.

>>> from fipy import *
>>> m = Grid1D(nx = 2)
>>> cv = CellVariable(mesh = m)
>>> fv = FaceVariable(mesh = m)
>>> vcv = CellVariable(mesh=m, rank=1)
>>> vfv = FaceVariable(mesh=m, rank=1)
>>> __ConvectionTerm(coeff = cv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = fv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = vcv)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = vfv)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = (1,))
__ConvectionTerm(coeff=(1,))
>>> ExplicitUpwindConvectionTerm(coeff = (0,)).solve(var=cv, solver=DummySolver()) 
Traceback (most recent call last):
    ...
TransientTermError: The equation requires a TransientTerm with explicit convection.
>>> (TransientTerm(0.) - ExplicitUpwindConvectionTerm(coeff = (0,))).solve(var=cv, solver=DummySolver(), dt=1.)

>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = 1)).solve(var=cv, solver=DummySolver(), dt=1.) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> m2 = Grid2D(nx=2, ny=1)
>>> cv2 = CellVariable(mesh=m2)
>>> vcv2 = CellVariable(mesh=m2, rank=1)
>>> vfv2 = FaceVariable(mesh=m2, rank=1)
>>> __ConvectionTerm(coeff=vcv2)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> __ConvectionTerm(coeff=vfv2)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = ((0,), (0,)))).solve(var=cv2, solver=DummySolver(), dt=1.)
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = (0, 0))).solve(var=cv2, solver=DummySolver(), dt=1.)
Parameters:

coeff (The Term’s coefficient value.) –

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

exception fipy.IllConditionedPreconditionerWarning(solver, iter, relres)

Bases: PreconditionerWarning

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

fipy.ImplicitDiffusionTerm

alias of DiffusionTerm

class fipy.ImplicitSourceTerm(coeff=0.0, var=None)

Bases: SourceTerm

The ImplicitSourceTerm represents

\int_V \phi S \,dV \simeq \phi_P S_P V_P

where S is the coeff value.

Create a Term.

Parameters:

coeff (float or CellVariable or FaceVariable) – Coefficient for the term. FaceVariable objects are only acceptable for diffusion or convection terms.

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

exception fipy.IncorrectSolutionVariable(s='The solution variable is incorrect.')

Bases: Exception

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

fipy.L1error(var, matrix, RHSvector)

\frac{\|\mathtt{var} - \mathtt{var}^\text{old}\|_1} {\|\mathtt{var}^\text{old}\|_1}

where \|\vec{x}\|_1 is the L^1 norm of \vec{x}.

Parameters:

  • var (CellVariable) – The CellVariable in question.

  • matrix – (ignored)

  • RHSvector – (ignored)

fipy.L2error(var, matrix, RHSvector)

\frac{\|\mathtt{var} - \mathtt{var}^\text{old}\|_2} {\|\mathtt{var}^\text{old}\|_2}

where \|\vec{x}\|_2 is the L^2 norm of \vec{x}.

Parameters:

  • var (CellVariable) – The CellVariable in question.

  • matrix – (ignored)

  • RHSvector – (ignored)

fipy.LINFerror(var, matrix, RHSvector)

\frac{\|\mathtt{var} - \mathtt{var}^\text{old}\|_\infty} {\|\mathtt{var}^\text{old}\|_\infty}

where \|\vec{x}\|_\infty is the L^\infty norm of \vec{x}.

Parameters:

  • var (CellVariable) – The CellVariable in question.

  • matrix – (ignored)

  • RHSvector – (ignored)

class fipy.LinearBicgSolver(tolerance=1e-10, iterations=1000, precon=None)

Bases: PETScKrylovSolver

The LinearBicgSolver is an interface to the biconjugate gradient solver in PETSc, using no preconditioner by default.

Parameters:

  • tolerance: The required error tolerance.

  • iterations: The maximum number of iterative steps to perform.

  • precon: Preconditioner to use (string).

__del__()
__enter__()
__exit__(exc_type, exc_value, traceback)
__repr__()

Return repr(self).

solver = 'bicg'
class fipy.LinearCGSSolver(tolerance=1e-10, iterations=1000, precon=None)

Bases: PETScKrylovSolver

The LinearCGSSolver is an interface to the conjugate gradient squared solver in PETSc, using no preconditioner by default.

Parameters:

  • tolerance: The required error tolerance.

  • iterations: The maximum number of iterative steps to perform.

  • precon: Preconditioner to use (string).

__del__()
__enter__()
__exit__(exc_type, exc_value, traceback)
__repr__()

Return repr(self).

solver = 'cgs'
class fipy.LinearGMRESSolver(tolerance=1e-10, iterations=1000, precon=None)

Bases: PETScKrylovSolver

The LinearGMRESSolver is an interface to the GMRES solver in PETSc, using no preconditioner by default.

Parameters:

  • tolerance: The required error tolerance.

  • iterations: The maximum number of iterative steps to perform.

  • precon: Preconditioner to use (string).

__del__()
__enter__()
__exit__(exc_type, exc_value, traceback)
__repr__()

Return repr(self).

solver = 'gmres'
class fipy.LinearLUSolver(tolerance=1e-10, iterations=10, precon='lu')

Bases: PETScSolver

The LinearLUSolver is an interface to the LU preconditioner in PETSc. A direct solve is performed.

Parameters:

  • tolerance: The required error tolerance.

  • iterations: The maximum number of iterative steps to perform.

  • precon: Ignored.

__del__()
__enter__()
__exit__(exc_type, exc_value, traceback)
__repr__()

Return repr(self).

class fipy.LinearPCGSolver(tolerance=1e-10, iterations=1000, precon=None)

Bases: PETScKrylovSolver

The LinearPCGSolver is an interface to the cg solver in PETSc, using no preconditioner by default.

Parameters:

  • tolerance: The required error tolerance.

  • iterations: The maximum number of iterative steps to perform.

  • precon: Preconditioner to use (string).

__del__()
__enter__()
__exit__(exc_type, exc_value, traceback)
__repr__()

Return repr(self).

solver = 'cg'
class fipy.Matplotlib1DViewer(vars, title=None, xlog=False, ylog=False, limits={}, legend='upper left', axes=None, **kwlimits)

Bases: AbstractMatplotlibViewer

Displays a y vs. x plot of one or more 1D CellVariable objects using Matplotlib.

>>> import fipy as fp
>>> mesh = fp.Grid1D(nx=100)
>>> x, = mesh.cellCenters
>>> xVar = fp.CellVariable(mesh=mesh, name="x", value=x)
>>> k = fp.Variable(name="k", value=0.)
>>> viewer = Matplotlib1DViewer(vars=(fp.numerix.sin(k * xVar) + 2,
...                               fp.numerix.cos(k * xVar / fp.numerix.pi) + 2),
...                 xmin=10, xmax=90,
...                 datamin=1.1, datamax=4.0,
...                 title="Matplotlib1DViewer test")
>>> for kval in fp.numerix.arange(0, 0.3, 0.03):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer.cmap = "ocean"
>>> viewer.log = True

>>> viewer.title = "Matplotlib1DViewer changed"
>>> viewer.plot()
>>> viewer._promptForOpinion()
Parameters:

  • vars (CellVariable or list) – CellVariable objects to plot

  • title (str, optional) – displayed at the top of the Viewer window

  • xlog (bool) – log scaling of x axis if True

  • ylog (bool) – log scaling of y axis if True

  • limits (dict) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale. (ymin and ymax are synonyms for datamin and datamax).

  • xmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale. (ymin and ymax are synonyms for datamin and datamax).

  • datamin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale. (ymin and ymax are synonyms for datamin and datamax).

  • datamax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale. (ymin and ymax are synonyms for datamin and datamax).

  • legend (str) – place a legend at the specified position, if not None

  • axes (Axes) – if not None, vars will be plotted into this Matplotlib Axes object

property axes

The Matplotlib Axes.

property cmap

The Matplotlib Colormap.

property colorbar

The Matplotlib Colorbar.

property fig

The Matplotlib Figure.

figaspect(figaspect, colorbar)
property id

The Matplotlib Figure number.

property limits
property lines

The collection of Matplotlib Line2D objects representing the plotted data.

property log

Whether data has logarithmic scaling

plot(filename=None)

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

class fipy.Matplotlib2DContourViewer(vars, title=None, limits={}, cmap=None, colorbar='vertical', axes=None, number=None, levels=None, figaspect='auto', **kwlimits)

Bases: AbstractMatplotlib2DViewer

Displays a contour plot of a 2D CellVariable object.

The Matplotlib2DContourViewer plots a 2D CellVariable using Matplotlib.

Creates a Matplotlib2DContourViewer.

Parameters:

  • vars (CellVariable) – Variable to display

  • title (str, optional) – displayed at the top of the Viewer window

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • cmap (Colormap, optional) – the Colormap. Defaults to matplotlib.cm.jet

  • colorbar (bool, optional) – plot a color bar in specified orientation if not None

  • axes (Axes, optional) – if not None, vars will be plotted into this Matplotlib Axes object

  • number (int, optional) – Determines the number and positions of the contour lines / regions. (deprecated, use levels=).

  • levels (int or array-like, optional) – Determines the number and positions of the contour lines / regions. If an int n, tries to automatically choose no more than n+1 “nice” contour levels over the range of vars. If array-like, draw contour lines at the specified levels. The values must be in increasing order. E.g. to draw just the zero contour pass levels=[0].

  • figaspect (float) – desired aspect ratio of figure. If arg is a number, use that aspect ratio. If arg is auto, the aspect ratio will be determined from the Variable’s mesh.

property axes

The Matplotlib Axes.

property cmap

The Matplotlib Colormap.

property colorbar

The Matplotlib Colorbar.

property fig

The Matplotlib Figure.

figaspect(figaspect, colorbar)
property id

The Matplotlib Figure number.

property levels

The number of automatically-chosen contours or their values.

property limits
property log

Whether data has logarithmic scaling (bool).

plot(filename=None)

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

class fipy.Matplotlib2DGridContourViewer(vars, title=None, limits={}, cmap=None, colorbar='vertical', axes=None, levels=None, figaspect='auto', **kwlimits)

Bases: AbstractMatplotlib2DViewer

Displays a contour plot of a 2D CellVariable object.

The Matplotlib2DGridContourViewer plots a 2D CellVariable using Matplotlib.

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=50, ny=100, dx=0.1, dy=0.01)
>>> x, y = mesh.cellCenters
>>> xyVar = fp.CellVariable(mesh=mesh, name="x y", value=x * y)
>>> k = fp.Variable(name="k", value=0.)
>>> viewer = Matplotlib2DGridContourViewer(vars=fp.numerix.sin(k * xyVar) * 1000 + 1002,
...                 ymin=0.1, ymax=0.9,
...                 # datamin=1.1, datamax=4.0,
...                 title="Matplotlib2DGridContourViewer test")
>>> from builtins import range
>>> for kval in range(10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer.levels = 2

>>> viewer.cmap = "ocean"
>>> viewer.log = True

>>> viewer.title = "Matplotlib2DGridContourViewer changed"
>>> viewer.plot()
>>> viewer._promptForOpinion()

Creates a Matplotlib2DViewer.

Parameters:

  • vars (CellVariable) – the Variable to display.

  • title (str, optional) – displayed at the top of the Viewer window

  • limits (dict) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • cmap (Colormap, optional) – the Colormap. Defaults to matplotlib.cm.jet

  • colorbar (bool, optional) – plot a color bar if not None

  • axes (Axes, optional) – if not None, vars will be plotted into this Matplotlib Axes object

  • levels (int or array-like, optional) – Determines the number and positions of the contour lines / regions. If an int n, tries to automatically choose no more than n+1 “nice” contour levels over the range of vars. If array-like, draw contour lines at the specified levels. The values must be in increasing order. E.g. to draw just the zero contour pass levels=[0].

  • figaspect (float, optional) – desired aspect ratio of figure. If a number, use that aspect ratio. If auto, the aspect ratio will be determined from the vars’s mesh.

property axes

The Matplotlib Axes.

property cmap

The Matplotlib Colormap.

property colorbar

The Matplotlib Colorbar.

property fig

The Matplotlib Figure.

figaspect(figaspect, colorbar)
property id

The Matplotlib Figure number.

property levels

The number of automatically-chosen contours or their values.

property limits
property log

Whether data has logarithmic scaling (bool).

plot(filename=None)

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

class fipy.Matplotlib2DGridViewer(vars, title=None, limits={}, cmap=None, colorbar='vertical', axes=None, figaspect='auto', **kwlimits)

Bases: AbstractMatplotlib2DViewer

Displays an image plot of a 2D CellVariable object using Matplotlib.

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=50, ny=100, dx=0.1, dy=0.01)
>>> x, y = mesh.cellCenters
>>> xyVar = fp.CellVariable(mesh=mesh, name="x y", value=x * y)
>>> k = fp.Variable(name="k", value=0.)
>>> viewer = Matplotlib2DGridViewer(vars=fp.numerix.sin(k * xyVar) * 1000 + 1002,
...                 ymin=0.1, ymax=0.9,
...                 # datamin=1.1, datamax=4.0,
...                 title="Matplotlib2DGridViewer test")
>>> from builtins import range
>>> for kval in range(10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer.cmap = "ocean"
>>> viewer.log = True

>>> viewer.title = "Matplotlib2DGridViewer changed"
>>> viewer.plot()
>>> viewer._promptForOpinion()

Creates a Matplotlib2DGridViewer.

Parameters:

  • vars (CellVariable) – the Variable to display.

  • title (str, optional) – displayed at the top of the Viewer window

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • cmap (Colormap, optional) – the Colormap. Defaults to matplotlib.cm.jet

  • xmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • colorbar (bool, optional) – plot a color bar in specified orientation if not None

  • axes (Axes, optional) – if not None, vars will be plotted into this Matplotlib Axes object

  • figaspect (float, optional) – desired aspect ratio of figure. If arg is a number, use that aspect ratio. If arg is auto, the aspect ratio will be determined from the Variable’s mesh.

property axes

The Matplotlib Axes.

property cmap

The Matplotlib Colormap.

property colorbar

The Matplotlib Colorbar.

property fig

The Matplotlib Figure.

figaspect(figaspect, colorbar)
property id

The Matplotlib Figure number.

property limits
property log

Whether data has logarithmic scaling (bool).

plot(filename=None)

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

class fipy.Matplotlib2DViewer(vars, title=None, limits={}, cmap=None, colorbar='vertical', axes=None, figaspect='auto', **kwlimits)

Bases: AbstractMatplotlib2DViewer

Displays a contour plot of a 2D CellVariable object.

The Matplotlib2DViewer plots a 2D CellVariable using Matplotlib.

>>> import fipy as fp
>>> mesh = (fp.Grid2D(nx=5, ny=10, dx=0.1, dy=0.1)
...         + (fp.Tri2D(nx=5, ny=5, dx=0.1, dy=0.1)
...          + ((0.5,), (0.2,))))
>>> x, y = mesh.cellCenters
>>> xyVar = fp.CellVariable(mesh=mesh, name="x y", value=x * y)
>>> k = fp.Variable(name="k", value=0.)
>>> viewer = Matplotlib2DViewer(vars=fp.numerix.sin(k * xyVar) * 1000 + 1002,
...                 ymin=0.1, ymax=0.9,
...                 # datamin=1.1, datamax=4.0,
...                 title="Matplotlib2DViewer test")
>>> from builtins import range
>>> for kval in range(10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer.cmap = "ocean"
>>> viewer.log = True

>>> viewer.title = "Matplotlib2DViewer changed"
>>> viewer.plot()
>>> viewer._promptForOpinion()

Creates a Matplotlib2DViewer.

Parameters:

  • vars (CellVariable) – the Variable to display.

  • title (str, optional) – displayed at the top of the Viewer window

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • cmap (Colormap, optional) – the Colormap. Defaults to matplotlib.cm.jet

  • xmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • colorbar (bool, optional) – plot a color bar in specified orientation if not None

  • axes (Axes, optional) – if not None, vars will be plotted into this Matplotlib Axes object

  • figaspect (float, optional) – desired aspect ratio of figure. If arg is a number, use that aspect ratio. If arg is auto, the aspect ratio will be determined from the Variable’s mesh.

property axes

The Matplotlib Axes.

property cmap

The Matplotlib Colormap.

property collection

The Matplotlib PolyCollection representing the cells.

property colorbar

The Matplotlib Colorbar.

property fig

The Matplotlib Figure.

figaspect(figaspect, colorbar)
property id

The Matplotlib Figure number.

property limits
property log

Whether data has logarithmic scaling (bool).

plot(filename=None)

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

class fipy.MatplotlibStreamViewer(vars, title=None, log=False, limits={}, axes=None, figaspect='auto', density=1, linewidth=None, color=None, cmap=None, norm=None, arrowsize=1, arrowstyle='-|>', minlength=0.1, **kwlimits)

Bases: AbstractMatplotlib2DViewer

Displays a stream plot of a 2D rank-1 CellVariable or FaceVariable object using Matplotlib

One issue is that this Viewer relies on scipy.interpolate.griddata, which interpolates on the convex hull of the data. The results is that streams are plotted across any concavities in the mesh.

Another issue is that it does not seem possible to remove the streams without calling cla(), which means that different set of streams cannot be overlaid.

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=50, ny=100, dx=0.1, dy=0.01)
>>> x, y = mesh.cellCenters
>>> xyVar = fp.CellVariable(mesh=mesh, name="x y", value=x * y)
>>> k = fp.Variable(name="k", value=1.)
>>> viewer = MatplotlibStreamViewer(vars=fp.numerix.sin(k * xyVar).grad,
...                 title="MatplotlibStreamViewer test")
>>> for kval in fp.numerix.arange(1, 10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer = MatplotlibStreamViewer(vars=fp.numerix.sin(k * xyVar).faceGrad,
...                 title="MatplotlibStreamViewer test")
>>> from builtins import range
>>> for kval in range(10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer.cmap = "ocean"
>>> viewer.log = True

>>> viewer.title = "MatplotlibStreamViewer changed"
>>> viewer.plot()
>>> viewer._promptForOpinion()

>>> import fipy as fp
>>> mesh = (fp.Grid2D(nx=5, ny=10, dx=0.1, dy=0.1)
...         + (fp.Tri2D(nx=5, ny=5, dx=0.1, dy=0.1)
...          + ((0.5,), (0.2,))))
>>> x, y = mesh.cellCenters
>>> xyVar = fp.CellVariable(mesh=mesh, name="x y", value=x * y)
>>> k = fp.Variable(name="k", value=1.)
>>> viewer = MatplotlibStreamViewer(vars=fp.numerix.sin(k * xyVar).grad,
...                 title="MatplotlibStreamViewer test")
>>> for kval in fp.numerix.arange(1, 10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer = MatplotlibStreamViewer(vars=fp.numerix.sin(k * xyVar).faceGrad,
...                 title="MatplotlibStreamViewer test")
>>> from builtins import range
>>> for kval in range(10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer.cmap = "ocean"
>>> viewer.log = True

>>> viewer.title = "MatplotlibStreamViewer changed"
>>> viewer.plot()
>>> viewer._promptForOpinion()

Creates a MatplotlibStreamViewer.

Parameters:

  • vars (CellVariable or FaceVariable) – rank-1 Variable to display

  • title (str, optional) – displayed at the top of the Viewer window

  • log (bool, optional) – if True, arrow length goes at the base-10 logarithm of the magnitude

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • xmax (float) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymin (float) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymax (float) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamin (float) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamax (float) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • axes (Axes, optional) – if not None, vars will be plotted into this Matplotlib Axes object

  • figaspect (float, optional) – desired aspect ratio of figure. If arg is a number, use that aspect ratio. If arg is auto, the aspect ratio will be determined from the Variable’s mesh.

  • density (float or (float, float), optional) – Controls the closeness of streamlines. When density = 1, the domain is divided into a 30x30 grid. density linearly scales this grid. Each cell in the grid can have, at most, one traversing streamline. For different densities in each direction, use a tuple (density_x, density_y).

  • linewidth (array_like or CellVariable or FaceVariable, optional) – The width of the stream lines. With a rank-0 CellVariable or FaceVariable the line width can be varied across the grid. The MeshVariable must have the same type and be defined on the same Mesh as vars.

  • color (str or CellVariable or FaceVariable, optional) – The streamline color as a matplotlib color code or a field of numbers. If given a rank-0 CellVariable or FaceVariable, its values are converted to colors using cmap and norm. The MeshVariable must have the same type and be defined on the same Mesh as vars.

  • cmap (Colormap, optional) – Colormap used to plot streamlines and arrows. This is only used if color is a MeshVariable.

  • norm (Normalize, optional) – Normalize object used to scale luminance data to 0, 1. If None, stretch (min, max) to (0, 1). Only necessary when color is a MeshVariable.

  • arrowsize (float, optional) – Scaling factor for the arrow size.

  • arrowstyle (str, optional) – Arrow style specification. See ~matplotlib.patches.FancyArrowPatch.

  • minlength (float, optional) – Minimum length of streamline in axes coordinates.

property axes

The Matplotlib Axes.

property cmap

The Matplotlib Colormap.

property colorbar

The Matplotlib Colorbar.

property fig

The Matplotlib Figure.

figaspect(figaspect, colorbar)
property id

The Matplotlib Figure number.

property kwargs

keyword arguments to pass to streamplot().

property limits
property log

Whether data has logarithmic scaling (bool).

plot(filename=None)

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

class fipy.MatplotlibVectorViewer(vars, title=None, scale=None, sparsity=None, log=False, limits={}, axes=None, figaspect='auto', **kwlimits)

Bases: AbstractMatplotlib2DViewer

Displays a vector plot of a 2D rank-1 CellVariable or FaceVariable object using Matplotlib

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=50, ny=100, dx=0.1, dy=0.01)
>>> x, y = mesh.cellCenters
>>> xyVar = fp.CellVariable(mesh=mesh, name="x y", value=x * y)
>>> k = fp.Variable(name="k", value=1.)
>>> viewer = MatplotlibVectorViewer(vars=fp.numerix.sin(k * xyVar).grad,
...                 title="MatplotlibVectorViewer test")
>>> for kval in fp.numerix.arange(1, 10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer = MatplotlibVectorViewer(vars=fp.numerix.sin(k * xyVar).faceGrad,
...                 title="MatplotlibVectorViewer test")
>>> from builtins import range
>>> for kval in range(10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> for sparsity in numerix.arange(5000, 0, -500):
...     viewer.quiver(sparsity=sparsity)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer.cmap = "ocean"
>>> viewer.log = True

>>> viewer.title = "MatplotlibVectorViewer changed"
>>> viewer.plot()
>>> viewer._promptForOpinion()

>>> import fipy as fp
>>> mesh = (fp.Grid2D(nx=5, ny=10, dx=0.1, dy=0.1)
...         + (fp.Tri2D(nx=5, ny=5, dx=0.1, dy=0.1)
...          + ((0.5,), (0.2,))))
>>> x, y = mesh.cellCenters
>>> xyVar = fp.CellVariable(mesh=mesh, name="x y", value=x * y)
>>> k = fp.Variable(name="k", value=1.)
>>> viewer = MatplotlibVectorViewer(vars=fp.numerix.sin(k * xyVar).grad,
...                 title="MatplotlibVectorViewer test")
>>> for kval in fp.numerix.arange(1, 10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer = MatplotlibVectorViewer(vars=fp.numerix.sin(k * xyVar).faceGrad,
...                 title="MatplotlibVectorViewer test")
>>> from builtins import range
>>> for kval in range(10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> for sparsity in numerix.arange(5000, 0, -500):
...     viewer.quiver(sparsity=sparsity)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> viewer.cmap = "ocean"
>>> viewer.log = True

>>> viewer.title = "MatplotlibVectorViewer changed"
>>> viewer.plot()
>>> viewer._promptForOpinion()

Creates a Matplotlib2DViewer.

Parameters:

  • vars (CellVariable or FaceVariable) – rank-1 Variable to display

  • title (str, optional) – displayed at the top of the Viewer window

  • scale (float, optional) – if not None, scale all arrow lengths by this value

  • sparsity (int, optional) – if not None, then this number of arrows will be randomly chosen (weighted by the cell volume or face area)

  • log (bool, optional) – if True, arrow length goes at the base-10 logarithm of the magnitude

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • axes (Axes, optional) – if not None, vars will be plotted into this Matplotlib Axes object

  • figaspect (float, optional) – desired aspect ratio of figure. If arg is a number, use that aspect ratio. If arg is auto, the aspect ratio will be determined from the Variable’s mesh.

property axes

The Matplotlib Axes.

property cmap

The Matplotlib Colormap.

property colorbar

The Matplotlib Colorbar.

property fig

The Matplotlib Figure.

figaspect(figaspect, colorbar)
property id

The Matplotlib Figure number.

property limits
property log

Whether data has logarithmic scaling (bool).

plot(filename=None)

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

quiver(sparsity=None, scale=None)
setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

fipy.MatplotlibViewer(vars, title=None, limits={}, cmap=None, colorbar='vertical', axes=None, **kwlimits)

Generic function for creating a MatplotlibViewer.

The MatplotlibViewer factory will search the module tree and return an instance of the first MatplotlibViewer it finds of the correct dimension and rank.

It is possible to view different Variables against different Matplotlib Axes

>>> from matplotlib import pyplot as plt
>>> from fipy import *

>>> plt.ion()
>>> fig = plt.figure()

>>> ax1 = plt.subplot((221))
>>> ax2 = plt.subplot((223))
>>> ax3 = plt.subplot((224))

>>> k = Variable(name="k", value=0.)

>>> mesh1 = Grid1D(nx=100)
>>> x, = mesh1.cellCenters
>>> xVar = CellVariable(mesh=mesh1, name="x", value=x)
>>> viewer1 = MatplotlibViewer(vars=(numerix.sin(0.1 * k * xVar), numerix.cos(0.1 * k * xVar / numerix.pi)),
...                            limits={'xmin': 10, 'xmax': 90},
...                            datamin=-0.9, datamax=2.0,
...                            title="Grid1D test",
...                            axes=ax1,
...                            legend=None)

>>> mesh2 = Grid2D(nx=50, ny=100, dx=0.1, dy=0.01)
>>> x, y = mesh2.cellCenters
>>> xyVar = CellVariable(mesh=mesh2, name="x y", value=x * y)
>>> viewer2 = MatplotlibViewer(vars=numerix.sin(k * xyVar),
...                            limits={'ymin': 0.1, 'ymax': 0.9},
...                            datamin=-0.9, datamax=2.0,
...                            title="Grid2D test",
...                            axes=ax2,
...                            colorbar=None)

>>> mesh3 = (Grid2D(nx=5, ny=10, dx=0.1, dy=0.1)
...          + (Tri2D(nx=5, ny=5, dx=0.1, dy=0.1)
...             + ((0.5,), (0.2,))))
>>> x, y = mesh3.cellCenters
>>> xyVar = CellVariable(mesh=mesh3, name="x y", value=x * y)
>>> viewer3 = MatplotlibViewer(vars=numerix.sin(k * xyVar),
...                            limits={'ymin': 0.1, 'ymax': 0.9},
...                            datamin=-0.9, datamax=2.0,
...                            title="Irregular 2D test",
...                            axes=ax3,
...                            cmap = plt.cm.OrRd)

>>> viewer = MultiViewer(viewers=(viewer1, viewer2, viewer3))
>>> from builtins import range
>>> for kval in range(10):
...     k.setValue(kval)
...     viewer.plot()

>>> viewer._promptForOpinion()
Parameters:

  • vars (CellVariable or list) – the Variable objects to display.

  • title (str, optional) – displayed at the top of the Viewer window

  • limits (dict) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • cmap (Colormap, optional) – the Colormap. Defaults to matplotlib.cm.jet

  • colorbar (bool, optional) – plot a color bar in specified orientation if not None

  • axes (Axes, optional) – if not None, vars will be plotted into this Matplotlib Axes object

exception fipy.MatrixIllConditionedWarning(solver, iter, relres)

Bases: SolverConvergenceWarning

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

exception fipy.MaximumIterationWarning(solver, iter, relres)

Bases: SolverConvergenceWarning

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class fipy.MayaviClient(vars, title=None, daemon_file=None, fps=1.0, **kwlimits)

Bases: AbstractViewer

The MayaviClient uses the Mayavi python plotting package.

>>> import fipy as fp
>>> mesh = fp.Grid1D(nx=100)
>>> x, = mesh.cellCenters
>>> xVar = fp.CellVariable(mesh=mesh, name="x", value=x)
>>> k = fp.Variable(name="k", value=0.)
>>> viewer = MayaviClient(vars=(fp.numerix.sin(k * xVar) + 2,
...                               fp.numerix.cos(k * xVar / fp.numerix.pi) + 2),
...                 xmin=10, xmax=90,
...                 datamin=1.1, datamax=4.0,
...                 title="MayaviClient test")
>>> for kval in fp.numerix.arange(0, 0.3, 0.03):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=50, ny=100, dx=0.1, dy=0.01)
>>> x, y = mesh.cellCenters
>>> xyVar = fp.CellVariable(mesh=mesh, name="x y", value=x * y)
>>> k = fp.Variable(name="k", value=0.)
>>> viewer = MayaviClient(vars=fp.numerix.sin(k * xyVar) * 1000 + 1002,
...                 ymin=0.1, ymax=0.9,
...                 # datamin=1.1, datamax=4.0,
...                 title="MayaviClient test")
>>> from builtins import range
>>> for kval in range(10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> import fipy as fp
>>> mesh = (fp.Grid2D(nx=5, ny=10, dx=0.1, dy=0.1)
...         + (fp.Tri2D(nx=5, ny=5, dx=0.1, dy=0.1)
...          + ((0.5,), (0.2,))))
>>> x, y = mesh.cellCenters
>>> xyVar = fp.CellVariable(mesh=mesh, name="x y", value=x * y)
>>> k = fp.Variable(name="k", value=0.)
>>> viewer = MayaviClient(vars=fp.numerix.sin(k * xyVar) * 1000 + 1002,
...                 ymin=0.1, ymax=0.9,
...                 # datamin=1.1, datamax=4.0,
...                 title="MayaviClient test")
>>> from builtins import range
>>> for kval in range(10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

>>> import fipy as fp
>>> mesh = fp.Grid3D(nx=50, ny=100, nz=10, dx=0.1, dy=0.01, dz=0.1)
>>> x, y, z = mesh.cellCenters
>>> xyzVar = fp.CellVariable(mesh=mesh, name=r"x y z", value=x * y * z)
>>> k = fp.Variable(name="k", value=0.)
>>> viewer = MayaviClient(vars=fp.numerix.sin(k * xyzVar) + 2,
...                     ymin=0.1, ymax=0.9,
...                     datamin=1.1, datamax=4.0,
...                     title="MayaviClient test")
>>> from builtins import range
>>> for kval in range(10):
...     k.setValue(kval)
...     viewer.plot()
>>> viewer._promptForOpinion()

Create a MayaviClient.

Parameters:

  • vars (CellVariable or list) – CellVariable objects to plot

  • title (str, optional) – displayed at the top of the Viewer window

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • daemon_file (str, optional) – the path to the script to run the separate Mayavi viewer process. Defaults to fipy/viewers/mayaviViewer/mayaviDaemon.py

  • fps (float, optional) – frames per second to attempt to display

__del__()
property fps

The frames per second to attempt to display.

property limits
plot(filename=None)

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

exception fipy.MeshDimensionError

Bases: IndexError

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class fipy.ModularVariable(*args, **kwds)

Bases: CellVariable

The ModularVariable defines a variable that exists on the circle between -\pi and \pi

The following examples show how ModularVariable works. When subtracting the answer wraps back around the circle.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 2)
>>> from fipy.tools import numerix
>>> pi = numerix.pi
>>> v1 = ModularVariable(mesh = mesh, value = (2*pi/3, -2*pi/3))
>>> v2 = ModularVariable(mesh = mesh, value = -2*pi/3)
>>> print(numerix.allclose(v2 - v1, (2*pi/3, 0)))
1

Obtaining the arithmetic face value.

>>> print(numerix.allclose(v1.arithmeticFaceValue, (2*pi/3, pi, -2*pi/3)))
1

Obtaining the gradient.

>>> print(numerix.allclose(v1.grad, ((pi/3, pi/3),)))
1

Obtaining the gradient at the faces.

>>> print(numerix.allclose(v1.faceGrad, ((0, 2*pi/3, 0),)))
1

Obtaining the gradient at the faces but without modular arithmetic.

>>> print(numerix.allclose(v1.faceGradNoMod, ((0, -4*pi/3, 0),)))
1
Parameters:

  • mesh (Mesh) – the mesh that defines the geometry of this Variable

  • name (str) – the user-readable name of the Variable

  • value (float or array_like) – the initial value

  • rank (int) – the rank (number of dimensions) of each element of this Variable. Default: 0

  • elementshape (tuple of int) – the shape of each element of this variable Default: rank * (mesh.dim,)

  • unit (str or PhysicalUnit) – The physical units of the variable

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__(points=None, order=0, nearestCellIDs=None)

Interpolates the CellVariable to a set of points using a method that has a memory requirement on the order of Ncells by Npoints in general, but uses only Ncells when the CellVariable’s mesh is a UniformGrid object.

Tests

>>> from fipy import *
>>> m = Grid2D(nx=3, ny=2)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.))))
[ 0.5  1.5  1.5]
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.)), order=1))
[ 0.25  1.1   1.2 ]
>>> m0 = Grid2D(nx=2, ny=2, dx=1., dy=1.)
>>> m1 = Grid2D(nx=4, ny=4, dx=.5, dy=.5)
>>> x, y = m0.cellCenters
>>> v0 = CellVariable(mesh=m0, value=x * y)
>>> print(v0(m1.cellCenters.globalValue))
[ 0.25  0.25  0.75  0.75  0.25  0.25  0.75  0.75  0.75  0.75  2.25  2.25
  0.75  0.75  2.25  2.25]
>>> print(v0(m1.cellCenters.globalValue, order=1))
[ 0.125  0.25   0.5    0.625  0.25   0.375  0.875  1.     0.5    0.875
  1.875  2.25   0.625  1.     2.25   2.625]
Parameters:

  • points (tuple or list of tuple) – A point or set of points in the format (X, Y, Z)

  • order ({0, 1}) – The order of interpolation, default is 0

  • nearestCellIDs (array_like) – Optional argument if user can calculate own nearest cell IDs array, shape should be same as points

__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the CellVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new CellVariable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
property arithmeticFaceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

Adjusted for a ModularVariable

cacheMe(recursive=False)
property cellVolumeAverage

Return the cell-volume-weighted average of the CellVariable:

<\phi>_\text{vol} = \frac{\sum_\text{cells} \phi_\text{cell} V_\text{cell}} {\sum_\text{cells} V_\text{cell}}

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx = 3, ny = 1, dx = .5, dy = .1)
>>> var = CellVariable(value = (1, 2, 6), mesh = mesh)
>>> print(var.cellVolumeAverage)
3.0
constrain(value, where=None)

Constrains the CellVariable to value at a location specified by where.

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> v.constrain(0., where=m.facesLeft)
>>> v.faceGrad.constrain([1.], where=m.facesRight)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]

Changing the constraint changes the dependencies

>>> v.constrain(1., where=m.facesLeft)
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can be Variable

>>> c = Variable(0.)
>>> v.constrain(c, where=m.facesLeft)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> c.value = 1.
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can have a Variable mask.

>>> v = CellVariable(mesh=m)
>>> mask = FaceVariable(mesh=m, value=m.facesLeft)
>>> v.constrain(1., where=mask)
>>> print(v.faceValue)
[ 1.  0.  0.  0.]
>>> mask[:] = mask | m.facesRight
>>> print(v.faceValue)
[ 1.  0.  0.  1.]
property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Make an duplicate of the Variable

>>> a = Variable(value=3)
>>> b = a.copy()
>>> b
Variable(value=array(3))

The duplicate will not reflect changes made to the original

>>> a.setValue(5)
>>> b
Variable(value=array(3))

Check that this works for arrays.

>>> a = Variable(value=numerix.array((0, 1, 2)))
>>> b = a.copy()
>>> b
Variable(value=array([0, 1, 2]))
>>> a[1] = 3
>>> b
Variable(value=array([0, 1, 2]))
dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

property faceGrad

Return \nabla \phi as a rank-1 FaceVariable (second-order gradient). Adjusted for a ModularVariable

property faceGradAverage

Deprecated since version 3.3: use grad.arithmeticFaceValue() instead

Return \nabla \phi as a rank-1 FaceVariable using averaging for the normal direction(second-order gradient)

property faceGradNoMod

Return \nabla \phi as a rank-1 FaceVariable (second-order gradient). Not adjusted for a ModularVariable

property faceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
property gaussGrad

Return \frac{1}{V_P} \sum_f \vec{n} \phi_f A_f as a rank-1 CellVariable (first-order gradient).

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue

Concatenate and return values from all processors

When running on a single processor, the result is identical to value.

property grad

Return \nabla \phi as a rank-1 CellVariable (first-order gradient). Adjusted for a ModularVariable

property harmonicFaceValue

Returns a FaceVariable whose value corresponds to the harmonic interpolation of the adjacent cells:

\phi_f = \frac{\phi_1 \phi_2}{(\phi_2 - \phi_1) \frac{d_{f2}}{d_{12}} + \phi_1}

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (0.5 / 1.) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (1.0 / 3.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (5.0 / 55.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property leastSquaresGrad

Return \nabla \phi, which is determined by solving for \nabla \phi in the following matrix equation,

\nabla \phi \cdot \sum_f d_{AP}^2 \vec{n}_{AP} \otimes \vec{n}_{AP} = \sum_f d_{AP}^2 \left( \vec{n} \cdot \nabla \phi \right)_{AP}

The matrix equation is derived by minimizing the following least squares sum,

F \left( \phi_x, \phi_y \right) = \sqrt{\sum_f \left( d_{AP} \vec{n}_{AP} \cdot \nabla \phi - d_{AP} \left( \vec{n}_{AP} \cdot \nabla \phi \right)_{AP} \right)^2 }

Tests

>>> from fipy import Grid2D
>>> m = Grid2D(nx=2, ny=2, dx=0.1, dy=2.0)
>>> print(numerix.allclose(CellVariable(mesh=m, value=(0, 1, 3, 6)).leastSquaresGrad.globalValue, \
...                                     [[8.0, 8.0, 24.0, 24.0],
...                                      [1.2, 2.0, 1.2, 2.0]]))
True

>>> from fipy import Grid1D
>>> print(numerix.allclose(CellVariable(mesh=Grid1D(dx=(2.0, 1.0, 0.5)),
...                                     value=(0, 1, 2)).leastSquaresGrad.globalValue, [[0.461538461538, 0.8, 1.2]]))
True
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property minmodFaceValue

Returns a FaceVariable with a value that is the minimum of the absolute values of the adjacent cells. If the values are of opposite sign then the result is zero:

\phi_f = \begin{cases} \phi_1& \text{when $|\phi_1| \le |\phi_2|$},\\ \phi_2& \text{when $|\phi_2| < |\phi_1|$},\\ 0 & \text{when $\phi1 \phi2 < 0$} \end{cases}

>>> from fipy import *
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(1, 2)).minmodFaceValue)
[1 1 2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, -2)).minmodFaceValue)
[-1 -1 -2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, 2)).minmodFaceValue)
[-1  0  2]
property name
property numericValue
property old

Return the values of the CellVariable from the previous solution sweep.

Combinations of CellVariable’s should also return old values.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 2)
>>> from fipy.variables.cellVariable import CellVariable
>>> var1 = CellVariable(mesh = mesh, value = (2, 3), hasOld = 1)
>>> var2 = CellVariable(mesh = mesh, value = (3, 4))
>>> v = var1 * var2
>>> print(v)
[ 6 12]
>>> var1.value = ((3, 2))
>>> print(v)
[9 8]
>>> print(v.old)
[ 6 12]

The following small test is to correct for a bug when the operator does not just use variables.

>>> v1 = var1 * 3
>>> print(v1)
[9 6]
>>> print(v1.old)
[6 9]
put(indices, value)
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> c = Constraint(0., where=m.facesLeft)
>>> v.constrain(c)
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> v.release(constraint=c)
>>> print(v.faceValue)
[ 0.5  1.   2.   2.5]
setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
updateOld()

Set the values of the previous solution sweep to the current values. Test case due to bug.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 1)
>>> var = ModularVariable(mesh=mesh, value=1., hasOld=1)
>>> var.updateOld()
>>> var[:] = 2
>>> answer = CellVariable(mesh=mesh, value=1.)
>>> print(var.old.allclose(answer))
True
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
class fipy.MultiViewer(viewers)

Bases: AbstractViewer

Treat a collection of different viewers (such for different 2D plots or 1D plots with different axes) as a single viewer that will plot() all subviewers simultaneously.

Parameters:

viewers (list of ~fipy.viewers.viewer.Viewer) – the viewers to bind together

property limits
plot()

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

class fipy.NthOrderBoundaryCondition(faces, value, order)

Bases: BoundaryCondition

This boundary condition is generally used in conjunction with a ImplicitDiffusionTerm that has multiple coefficients. It does not have any direct effect on the solution matrices, but its derivatives do.

Creates an NthOrderBoundaryCondition.

Parameters:

  • faces (FaceVariable of bool) – Mask of faces where this condition applies.

  • value (float) – Value to impose.

  • order (int) – Order of the boundary condition. An order of 0 corresponds to a FixedValue and an order of 1 corresponds to a FixedFlux. Even and odd orders behave like FixedValue and FixedFlux objects, respectively, but apply to higher order terms.

__repr__()

Return repr(self).

class fipy.PeriodicGrid1D(dx=1.0, nx=None, overlap=2, *args, **kwargs)

Bases: NonUniformGrid1D

Creates a Periodic grid mesh.

>>> mesh = PeriodicGrid1D(dx = (1, 2, 3))

>>> print(numerix.allclose(numerix.nonzero(mesh.exteriorFaces)[0],
...                        [3])) 
True

>>> print(numerix.allclose(mesh.faceCellIDs.filled(-999),
...                        [[2, 0, 1, 2],
...                         [0, 1, 2, -999]])) 
True

>>> print(numerix.allclose(mesh._cellDistances,
...                        [ 2., 1.5, 2.5, 1.5])) 
True

>>> print(numerix.allclose(mesh._cellToCellDistances,
...                        [[ 2.,   1.5,  2.5],
...                         [ 1.5,  2.5,  2. ]])) 
True

>>> print(numerix.allclose(mesh.faceNormals,
...                        [[ 1.,  1.,  1.,  1.]])) 
True

>>> print(numerix.allclose(mesh._cellVertexIDs,
...                        [[1, 2, 2],
...                        [0, 1, 0]])) 
True
property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters

Defined outside of a geometry class since we need the CellVariable version of cellCenters; that is, the cellCenters defined in fipy.meshes.mesh and not in any geometry (since a CellVariable requires a reference to a mesh).

property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.PeriodicGrid2D(dx=1.0, dy=1.0, nx=None, ny=None, overlap=2, communicator=SerialPETScCommWrapper(), *args, **kwargs)

Bases: _BasePeriodicGrid2D

Creates a periodic 2D grid mesh with horizontal faces numbered first and then vertical faces. Vertices and cells are numbered in the usual way.

>>> from fipy import numerix

>>> mesh = PeriodicGrid2D(dx = 1., dy = 0.5, nx = 2, ny = 2)

>>> print(numerix.allclose(numerix.nonzero(mesh.exteriorFaces)[0],
...                        [ 4,  5,  8, 11]))  
True

>>> print(numerix.allclose(mesh.faceCellIDs.filled(-1),
...                        [[2, 3, 0, 1, 2, 3, 1, 0, 1, 3, 2, 3],
...                         [0, 1, 2, 3, -1, -1, 0, 1, -1, 2, 3, -1]])) 
True

>>> print(numerix.allclose(mesh._cellDistances,
...                        [ 0.5, 0.5, 0.5, 0.5, 0.25, 0.25, 1., 1., 0.5, 1., 1., 0.5])) 
True

>>> print(numerix.allclose(mesh.cellFaceIDs,
...                        [[0, 1, 2, 3],
...                         [7, 6, 10, 9],
...                         [2, 3, 0, 1],
...                         [6, 7, 9, 10]])) 
True

>>> print(numerix.allclose(mesh._cellToCellDistances,
...                        [[ 0.5, 0.5, 0.5, 0.5],
...                         [ 1., 1., 1., 1. ],
...                         [ 0.5, 0.5, 0.5, 0.5],
...                         [ 1., 1., 1., 1. ]])) 
True

>>> normals = [[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
...            [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]]

>>> print(numerix.allclose(mesh.faceNormals, normals)) 
True

>>> print(numerix.allclose(mesh._cellVertexIDs,
...                        [[4, 5, 7, 8],
...                         [3, 4, 6, 7],
...                         [1, 2, 4, 5],
...                         [0, 1, 3, 4]])) 
True
property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
extrude(extrudeFunc=<function Mesh2D.<lambda>>, layers=1)

This function returns a new 3D mesh. The 2D mesh is extruded using the extrudeFunc and the number of layers.

>>> from fipy.meshes.nonUniformGrid2D import NonUniformGrid2D
>>> print(NonUniformGrid2D(nx=2, ny=2).extrude(layers=2).cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]]

>>> from fipy.meshes.tri2D import Tri2D
>>> print(Tri2D().extrude(layers=2).cellCenters.allclose([[ 0.83333333, 0.5,        0.16666667, 0.5,       0.83333333, 0.5,
...                                                      0.16666667, 0.5       ],
...                                                       [ 0.5,        0.83333333, 0.5,        0.16666667, 0.5,        0.83333333,
...                                                      0.5,        0.16666667],
...                                                      [ 0.5,        0.5,        0.5,        0.5,        1.5,        1.5,        1.5,
...                                                      1.5       ]]))
True
Parameters:

  • extrudeFunc (function) – Takes the vertex coordinates and returns the displaced values

  • layers (int) – Number of layers in the extruded mesh (number of times extrudeFunc will be called)

property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.PeriodicGrid2DLeftRight(dx=1.0, dy=1.0, nx=None, ny=None, overlap=2, communicator=SerialPETScCommWrapper(), *args, **kwargs)

Bases: _BasePeriodicGrid2D

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
extrude(extrudeFunc=<function Mesh2D.<lambda>>, layers=1)

This function returns a new 3D mesh. The 2D mesh is extruded using the extrudeFunc and the number of layers.

>>> from fipy.meshes.nonUniformGrid2D import NonUniformGrid2D
>>> print(NonUniformGrid2D(nx=2, ny=2).extrude(layers=2).cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]]

>>> from fipy.meshes.tri2D import Tri2D
>>> print(Tri2D().extrude(layers=2).cellCenters.allclose([[ 0.83333333, 0.5,        0.16666667, 0.5,       0.83333333, 0.5,
...                                                      0.16666667, 0.5       ],
...                                                       [ 0.5,        0.83333333, 0.5,        0.16666667, 0.5,        0.83333333,
...                                                      0.5,        0.16666667],
...                                                      [ 0.5,        0.5,        0.5,        0.5,        1.5,        1.5,        1.5,
...                                                      1.5       ]]))
True
Parameters:

  • extrudeFunc (function) – Takes the vertex coordinates and returns the displaced values

  • layers (int) – Number of layers in the extruded mesh (number of times extrudeFunc will be called)

property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.PeriodicGrid2DTopBottom(dx=1.0, dy=1.0, nx=None, ny=None, overlap=2, communicator=SerialPETScCommWrapper(), *args, **kwargs)

Bases: _BasePeriodicGrid2D

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
extrude(extrudeFunc=<function Mesh2D.<lambda>>, layers=1)

This function returns a new 3D mesh. The 2D mesh is extruded using the extrudeFunc and the number of layers.

>>> from fipy.meshes.nonUniformGrid2D import NonUniformGrid2D
>>> print(NonUniformGrid2D(nx=2, ny=2).extrude(layers=2).cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]]

>>> from fipy.meshes.tri2D import Tri2D
>>> print(Tri2D().extrude(layers=2).cellCenters.allclose([[ 0.83333333, 0.5,        0.16666667, 0.5,       0.83333333, 0.5,
...                                                      0.16666667, 0.5       ],
...                                                       [ 0.5,        0.83333333, 0.5,        0.16666667, 0.5,        0.83333333,
...                                                      0.5,        0.16666667],
...                                                      [ 0.5,        0.5,        0.5,        0.5,        1.5,        1.5,        1.5,
...                                                      1.5       ]]))
True
Parameters:

  • extrudeFunc (function) – Takes the vertex coordinates and returns the displaced values

  • layers (int) – Number of layers in the extruded mesh (number of times extrudeFunc will be called)

property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.PeriodicGrid3D(dx=1.0, dy=1.0, dz=1.0, nx=None, ny=None, nz=None, overlap=2, communicator=SerialPETScCommWrapper(), *args, **kwargs)

Bases: _BasePeriodicGrid3D

Creates a periodic 3D grid mesh with horizontal faces numbered first and then vertical faces. Vertices and cells are numbered in the usual way.

>>> from fipy import numerix

>>> mesh = PeriodicGrid3D(dx=1., dy=0.5, dz=2., nx=2, ny=2, nz=1)
>>> print(numerix.allclose(numerix.nonzero(mesh.exteriorFaces)[0],
...                        [4, 5, 6, 7, 12, 13, 16, 19]))  
True

>>> print(numerix.allclose(mesh.faceCellIDs.filled(-1),
...                        [[0, 1, 2, 3, 0, 1, 2, 3, 2, 3,
...                          0, 1, 2, 3, 1, 0, 1, 3, 2, 3],
...                         [0, 1, 2, 3, -1, -1, -1, -1, 0, 1,
...                          2, 3, -1, -1, 0, 1, -1, 2, 3, -1]])) 
True

>>> print(numerix.allclose(mesh._cellDistances,
...                        [2., 2., 2., 2., 1., 1., 1., 1., 0.5, 0.5,
...                         0.5, 0.5, 0.25, 0.25, 1., 1., 0.5, 1., 1., 0.5])) 
True

>>> print(numerix.allclose(mesh.cellFaceIDs,
...                        [[14, 15, 17, 18],
...                         [15, 14, 18, 17],
...                         [8, 9, 10, 11],
...                         [10, 11, 8, 9],
...                         [0, 1, 2, 3],
...                         [0, 1, 2, 3]])) 
True

>>> print(numerix.allclose(mesh._cellToCellDistances,
...                        [[1., 1., 1., 1.],
...                         [1., 1., 1., 1.],
...                         [0.5, 0.5, 0.5, 0.5],
...                         [0.5, 0.5, 0.5, 0.5],
...                         [2., 2., 2., 2.],
...                         [2., 2., 2., 2.]])) 
True

>>> normals = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
...            [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
...            [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]

>>> print(numerix.allclose(mesh.faceNormals, normals)) 
True

>>> print(numerix.allclose(mesh._cellVertexIDs,
...                        [[13, 14, 16, 17],
...                         [12, 13, 15, 16],
...                         [10, 11, 13, 14],
...                         [9, 10, 12, 13],
...                         [4, 5, 7, 8],
...                         [3, 4, 6, 7],
...                         [1, 2, 4, 5],
...                         [0, 1, 3, 4]])) 
True
property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.PeriodicGrid3DFrontBack(dx=1.0, dy=1.0, dz=1.0, nx=None, ny=None, nz=None, overlap=2, communicator=SerialPETScCommWrapper(), *args, **kwargs)

Bases: _BasePeriodicGrid3D

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.PeriodicGrid3DLeftRight(dx=1.0, dy=1.0, dz=1.0, nx=None, ny=None, nz=None, overlap=2, communicator=SerialPETScCommWrapper(), *args, **kwargs)

Bases: _BasePeriodicGrid3D

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.PeriodicGrid3DLeftRightFrontBack(dx=1.0, dy=1.0, dz=1.0, nx=None, ny=None, nz=None, overlap=2, communicator=SerialPETScCommWrapper(), *args, **kwargs)

Bases: _BasePeriodicGrid3D

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.PeriodicGrid3DLeftRightTopBottom(dx=1.0, dy=1.0, dz=1.0, nx=None, ny=None, nz=None, overlap=2, communicator=SerialPETScCommWrapper(), *args, **kwargs)

Bases: _BasePeriodicGrid3D

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.PeriodicGrid3DTopBottom(dx=1.0, dy=1.0, dz=1.0, nx=None, ny=None, nz=None, overlap=2, communicator=SerialPETScCommWrapper(), *args, **kwargs)

Bases: _BasePeriodicGrid3D

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.PeriodicGrid3DTopBottomFrontBack(dx=1.0, dy=1.0, dz=1.0, nx=None, ny=None, nz=None, overlap=2, communicator=SerialPETScCommWrapper(), *args, **kwargs)

Bases: _BasePeriodicGrid3D

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.PhysicalField(value, unit=None, array=None)

Bases: object

Physical field or quantity with units

Physical Fields can be constructed in one of two ways:

  • PhysicalField(*value*, *unit*), where *value* is a number of arbitrary type and *unit* is a string containing the unit name

    >>> print(PhysicalField(value = 10., unit = 'm'))
10.0 m

  • PhysicalField(*string*), where *string* contains both the value and the unit. This form is provided to make interactive use more convenient

    >>> print(PhysicalField(value = "10. m"))
10.0 m

Dimensionless quantities, with a unit of 1, can be specified in several ways

>>> print(PhysicalField(value = "1"))
1.0 1
>>> print(PhysicalField(value = 2., unit = " "))
2.0 1
>>> print(PhysicalField(value = 2.))
2.0 1

Physical arrays are also possible (and are the reason this code was adapted from Konrad Hinsen’s original PhysicalQuantity). The value can be a Numeric array:

>>> a = numerix.array(((3., 4.), (5., 6.)))
>>> print(PhysicalField(value = a, unit = "m"))
[[ 3.  4.]
 [ 5.  6.]] m

or a tuple:

>>> print(PhysicalField(value = ((3., 4.), (5., 6.)), unit = "m"))
[[ 3.  4.]
 [ 5.  6.]] m

or as a single value to be applied to every element of a supplied array:

>>> print(PhysicalField(value = 2., unit = "m", array = a))
[[ 2.  2.]
 [ 2.  2.]] m

Every element in an array has the same unit, which is stored only once for the whole array.

__abs__()

Return the absolute value of the quantity. The unit is unchanged.

>>> print(abs(PhysicalField(((3., -2.), (-1., 4.)), 'm')))
[[ 3.  2.]
 [ 1.  4.]] m
__add__(other)

Add two physical quantities, so long as their units are compatible. The unit of the result is the unit of the first operand.

>>> print(PhysicalField(10., 'km') + PhysicalField(10., 'm'))
10.01 km
>>> print(PhysicalField(10., 'km') + PhysicalField(10., 'J'))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
__array__(t=None)

Return a dimensionless PhysicalField as a Numeric array.

>>> print(numerix.array(PhysicalField(((2., 3.), (4., 5.)), "m/m")))
[[ 2.  3.]
 [ 4.  5.]]

As a special case, fields with angular units are converted to base units (radians) and then assumed dimensionless.

>>> print(numerix.array(PhysicalField(((2., 3.), (4., 5.)), "deg")))
[[ 0.03490659  0.05235988]
 [ 0.06981317  0.08726646]]

If the array is not dimensionless, the numerical value in its base units is returned.

>>> numerix.array(PhysicalField(((2., 3.), (4., 5.)), "mm"))
array([[ 0.002,  0.003],
       [ 0.004,  0.005]])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> from fipy.tools.dimensions.physicalField import PhysicalField
>>> print(type(numerix.array([1.0, 2.0]) * PhysicalField([1.0, 2.0], unit="m")))
<class 'fipy.tools.dimensions.physicalField.PhysicalField'>

>>> print(type(numerix.array([1.0, 2.0]) * PhysicalField([1.0, 2.0])))
<class 'fipy.tools.dimensions.physicalField.PhysicalField'>

>>> from scipy.special import gamma as Gamma 
>>> print(isinstance(Gamma(PhysicalField([1.0, 2.0])), type(numerix.array(1)))) 
1
__bool__()

Test if the quantity is zero.

Should this only pass if the unit offset is zero?

__div__(other)

Divide two physical quantities. The unit of the result is the unit of the first operand divided by the unit of the second.

>>> print(PhysicalField(10., 'm') / PhysicalField(2., 's'))
5.0 m/s

As a special case, if the result is dimensionless, the value is returned without units, rather than with a dimensionless unit of 1. This facilitates passing physical quantities to packages such as Numeric that cannot use units, while ensuring the quantities have the desired units

>>> print((PhysicalField(1., 'inch')
...        / PhysicalField(1., 'mm')))
25.4
__eq__(other)

Return self==value.

__float__()

Return a dimensionless PhysicalField quantity as a float.

>>> float(PhysicalField("2. m/m"))
2.0

As a special case, quantities with angular units are converted to base units (radians) and then assumed dimensionless.

>>> print(numerix.round(float(PhysicalField("2. deg")), 6))
0.034907

If the quantity is not dimensionless, the conversion fails.

>>> float(PhysicalField("2. m"))
Traceback (most recent call last):
    ...
TypeError: Not possible to convert a PhysicalField with dimensions to float

Just as a Numeric array cannot be cast to float, neither can PhysicalField arrays

>>> float(PhysicalField(((2., 3.), (4., 5.)), "m/m")) 
Traceback (most recent call last):
    ...
TypeError: only ...-1 arrays can be converted to Python scalars
__ge__(other)

Return self>=value.

__getitem__(index)

Return the specified element of the array. The unit of the result will be the unit of the array.

>>> a = PhysicalField(((3., 4.), (5., 6.)), "m")
>>> print(a[1, 1])
6.0 m
__gt__(other)

Compare self to other, returning an array of Boolean values corresponding to the test against each element.

>>> a = PhysicalField(((3., 4.), (5., 6.)), "m")
>>> print(numerix.allclose(a > PhysicalField("13 ft"),
...                        [[False, True], [ True, True]]))
True

Appropriately formatted dimensional quantity strings can also be compared.

>>> print(numerix.allclose(a > "13 ft",
...                        [[False, True], [ True, True]]))
True

Arrays are compared element to element

>>> print(numerix.allclose(a > PhysicalField(((3., 13.), (17., 6.)), "ft"),
...                        [[ True, True], [False, True]]))
True

Units must be compatible

>>> print(a > PhysicalField("1 lb"))
Traceback (most recent call last):
    ...
TypeError: Incompatible units

And so must array dimensions

>>> print(a > PhysicalField(((3., 13., 4.), (17., 6., 2.)), "ft")) 
Traceback (most recent call last):
    ...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__hash__()

Return hash(self).

__le__(other)

Return self<=value.

__len__()
__lt__(other)

Return self<value.

__mod__(other)

Return the remainder of dividing two physical quantities. The unit of the result is the unit of the first operand divided by the unit of the second.

>>> print(PhysicalField(11., 'm') % PhysicalField(2., 's'))
1.0 m/s
__mul__(other)

Multiply two physical quantities. The unit of the result is the product of the units of the operands.

>>> print(PhysicalField(10., 'N') * PhysicalField(10., 'm') == PhysicalField(100., 'N*m'))
True

As a special case, if the result is dimensionless, the value is returned without units, rather than with a dimensionless unit of 1. This facilitates passing physical quantities to packages such as Numeric that cannot use units, while ensuring the quantities have the desired units.

>>> print((PhysicalField(10., 's') * PhysicalField(2., 'Hz')))
20.0
__ne__(other)

Return self!=value.

__neg__()

Return the negative of the quantity. The unit is unchanged.

>>> print(-PhysicalField(((3., -2.), (-1., 4.)), 'm'))
[[-3.  2.]
 [ 1. -4.]] m
__nonzero__()

Test if the quantity is zero.

Should this only pass if the unit offset is zero?

__pos__()
__pow__(other)

Raise a PhysicalField to a power. The unit is raised to the same power.

>>> print(PhysicalField(10., 'm')**2)
100.0 m**2
__radd__(other)

Add two physical quantities, so long as their units are compatible. The unit of the result is the unit of the first operand.

>>> print(PhysicalField(10., 'km') + PhysicalField(10., 'm'))
10.01 km
>>> print(PhysicalField(10., 'km') + PhysicalField(10., 'J'))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
__rdiv__(other)
__repr__()

Return representation of a physical quantity suitable for re-use

>>> PhysicalField(value = 3., unit = "eV")
PhysicalField(3.0,'eV')
__rmul__(other)

Multiply two physical quantities. The unit of the result is the product of the units of the operands.

>>> print(PhysicalField(10., 'N') * PhysicalField(10., 'm') == PhysicalField(100., 'N*m'))
True

As a special case, if the result is dimensionless, the value is returned without units, rather than with a dimensionless unit of 1. This facilitates passing physical quantities to packages such as Numeric that cannot use units, while ensuring the quantities have the desired units.

>>> print((PhysicalField(10., 's') * PhysicalField(2., 'Hz')))
20.0
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)

Assign the specified element of the array, performing appropriate conversions.

>>> a = PhysicalField(((3., 4.), (5., 6.)), "m")
>>> a[0, 1] = PhysicalField("6 ft")
>>> print(a)
[[ 3.      1.8288]
 [ 5.      6.    ]] m
>>> a[1, 0] = PhysicalField("2 min")
Traceback (most recent call last):
    ...
TypeError: Incompatible units
__str__()

Return human-readable form of a physical quantity

>>> print(PhysicalField(value = 3., unit = "eV"))
3.0 eV
__sub__(other)

Subtract two physical quantities, so long as their units are compatible. The unit of the result is the unit of the first operand.

>>> print(PhysicalField(10., 'km') - PhysicalField(10., 'm'))
9.99 km
>>> print(PhysicalField(10., 'km') - PhysicalField(10., 'J'))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
__truediv__(other)

Divide two physical quantities. The unit of the result is the unit of the first operand divided by the unit of the second.

>>> print(PhysicalField(10., 'm') / PhysicalField(2., 's'))
5.0 m/s

As a special case, if the result is dimensionless, the value is returned without units, rather than with a dimensionless unit of 1. This facilitates passing physical quantities to packages such as Numeric that cannot use units, while ensuring the quantities have the desired units

>>> print((PhysicalField(1., 'inch')
...        / PhysicalField(1., 'mm')))
25.4
add(other)

Add two physical quantities, so long as their units are compatible. The unit of the result is the unit of the first operand.

>>> print(PhysicalField(10., 'km') + PhysicalField(10., 'm'))
10.01 km
>>> print(PhysicalField(10., 'km') + PhysicalField(10., 'J'))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
allclose(other, atol=None, rtol=1e-08)

This function tests whether or not self and other are equal subject to the given relative and absolute tolerances. The formula used is:

| self - other | < atol + rtol * | other |

This means essentially that both elements are small compared to atol or their difference divided by other’s value is small compared to rtol.

allequal(other)

This function tests whether or not self and other are exactly equal.

arccos()

Return the inverse cosine of the PhysicalField in radians

>>> print(PhysicalField(0).arccos().allclose("1.57079632679 rad"))
1

The input PhysicalField must be dimensionless

>>> print(numerix.round(PhysicalField("1 m").arccos(), 6))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
arccosh()

Return the inverse hyperbolic cosine of the PhysicalField

>>> print(numerix.allclose(PhysicalField(2).arccosh(),
...                        1.31695789692))
1

The input PhysicalField must be dimensionless

>>> print(numerix.round(PhysicalField("1. m").arccosh(), 6))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
arcsin()

Return the inverse sine of the PhysicalField in radians

>>> print(PhysicalField(1).arcsin().allclose("1.57079632679 rad"))
1

The input PhysicalField must be dimensionless

>>> print(numerix.round(PhysicalField("1 m").arcsin(), 6))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
arctan()

Return the arctangent of the PhysicalField in radians

>>> print(numerix.round(PhysicalField(1).arctan(), 6))
0.785398

The input PhysicalField must be dimensionless

>>> print(numerix.round(PhysicalField("1 m").arctan(), 6))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
arctan2(other)

Return the arctangent of self divided by other in radians

>>> print(numerix.round(PhysicalField(2.).arctan2(PhysicalField(5.)), 6))
0.380506

The input PhysicalField objects must be in the same dimensions

>>> print(numerix.round(PhysicalField(2.54, "cm").arctan2(PhysicalField(1., "inch")), 6))
0.785398

>>> print(numerix.round(PhysicalField(2.).arctan2(PhysicalField("5. m")), 6))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
arctanh()

Return the inverse hyperbolic tangent of the PhysicalField

>>> print(PhysicalField(0.5).arctanh())
0.549306144334

The input PhysicalField must be dimensionless

>>> print(numerix.round(PhysicalField("1 m").arctanh(), 6))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
ceil()

Return the smallest integer greater than or equal to the PhysicalField.

>>> print(PhysicalField(2.2, "m").ceil())
3.0 m
conjugate()

Return the complex conjugate of the PhysicalField.

>>> print(PhysicalField(2.2 - 3j, "ohm").conjugate() == PhysicalField(2.2 + 3j, "ohm"))
True
convertToUnit(unit)

Changes the unit to unit and adjusts the value such that the combination is equivalent. The new unit is by a string containing its name. The new unit must be compatible with the previous unit of the object.

>>> e = PhysicalField('2.7 Hartree*Nav')
>>> e.convertToUnit('kcal/mol')
>>> print(e)
1694.27557621 kcal/mol
copy()

Make a duplicate.

>>> a = PhysicalField(1, unit = 'inch')
>>> b = a.copy()

The duplicate will not reflect changes made to the original

>>> a.convertToUnit('cm')
>>> print(a)
2.54 cm
>>> print(b)
1 inch

Likewise for arrays

>>> a = PhysicalField(numerix.array((0, 1, 2)), unit  = 'm')
>>> b = a.copy()
>>> a[0] = 3
>>> print(a)
[3 1 2] m
>>> print(b)
[0 1 2] m
cos()

Return the cosine of the PhysicalField

>>> print(numerix.round(PhysicalField(2*numerix.pi/6, "rad").cos(), 6))
0.5
>>> print(numerix.round(PhysicalField(60., "deg").cos(), 6))
0.5

The units of the PhysicalField must be an angle

>>> PhysicalField(60., "m").cos()
Traceback (most recent call last):
    ...
TypeError: Incompatible units
cosh()

Return the hyperbolic cosine of the PhysicalField

>>> PhysicalField(0.).cosh()
1.0

The units of the PhysicalField must be dimensionless

>>> PhysicalField(60., "m").cosh()
Traceback (most recent call last):
    ...
TypeError: Incompatible units
divide(other)

Divide two physical quantities. The unit of the result is the unit of the first operand divided by the unit of the second.

>>> print(PhysicalField(10., 'm') / PhysicalField(2., 's'))
5.0 m/s

As a special case, if the result is dimensionless, the value is returned without units, rather than with a dimensionless unit of 1. This facilitates passing physical quantities to packages such as Numeric that cannot use units, while ensuring the quantities have the desired units

>>> print((PhysicalField(1., 'inch')
...        / PhysicalField(1., 'mm')))
25.4
dot(other)

Return the dot product of self with other. The resulting unit is the product of the units of self and other.

>>> v = PhysicalField(((5., 6.), (7., 8.)), "m")
>>> print(PhysicalField(((1., 2.), (3., 4.)), "m").dot(v))
[ 26.  44.] m**2
floor()

Return the largest integer less than or equal to the PhysicalField.

>>> print(PhysicalField(2.2, "m").floor())
2.0 m
getsctype(default=None)

Returns the NumPy sctype of the underlying array.

>>> PhysicalField(1, 'm').getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> PhysicalField(1., 'm').getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> PhysicalField((1, 1.), 'm').getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
inBaseUnits()

Return the quantity with all units reduced to their base SI elements.

>>> e = PhysicalField('2.7 Hartree*Nav')
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inDimensionless()

Returns the numerical value of a dimensionless quantity.

>>> print(PhysicalField(((2., 3.), (4., 5.))).inDimensionless())
[[ 2.  3.]
 [ 4.  5.]]

It’s an error to convert a quantity with units

>>> print(PhysicalField(((2., 3.), (4., 5.)), "m").inDimensionless())
Traceback (most recent call last):
    ...
TypeError: Incompatible units
inRadians()

Converts an angular quantity to radians and returns the numerical value.

>>> print(PhysicalField(((2., 3.), (4., 5.)), "rad").inRadians())
[[ 2.  3.]
 [ 4.  5.]]
>>> print(PhysicalField(((2., 3.), (4., 5.)), "deg").inRadians())
[[ 0.03490659  0.05235988]
 [ 0.06981317  0.08726646]]

As a special case, assumes a dimensionless quantity is already in radians.

>>> print(PhysicalField(((2., 3.), (4., 5.))).inRadians())
[[ 2.  3.]
 [ 4.  5.]]

It’s an error to convert a quantity with non-angular units

>>> print(PhysicalField(((2., 3.), (4., 5.)), "m").inRadians())
Traceback (most recent call last):
    ...
TypeError: Incompatible units
inSIUnits()

Return the quantity with all units reduced to SI-compatible elements.

>>> e = PhysicalField('2.7 Hartree*Nav')
>>> print(e.inSIUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more PhysicalField objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single PhysicalField.

>>> freeze = PhysicalField('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of PhysicalField instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = PhysicalField(314159., 's')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
isCompatible(unit)
itemset(value)

Assign the value of a scalar array, performing appropriate conversions.

>>> a = PhysicalField(4., "m")
>>> a.itemset(PhysicalField("6 ft"))
>>> print(a.allclose("1.8288 m"))
1
>>> a = PhysicalField(((3., 4.), (5., 6.)), "m")
>>> try: 
...     a.itemset(PhysicalField("6 ft"))
... except IndexError:
...     # NumPy 1.7 has changed the exception type
...     raise ValueError("can only place a scalar for an  array of size 1")
Traceback (most recent call last):
    ...
ValueError: can only convert an array of size 1 to a Python scalar
>>> a.itemset(PhysicalField("2 min"))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
property itemsize
log()

Return the natural logarithm of the PhysicalField

>>> print(numerix.round(PhysicalField(10).log(), 6))
2.302585

The input PhysicalField must be dimensionless

>>> print(numerix.round(PhysicalField("1. m").log(), 6))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
log10()

Return the base-10 logarithm of the PhysicalField

>>> print(numerix.round(PhysicalField(10.).log10(), 6))
1.0

The input PhysicalField must be dimensionless

>>> print(numerix.round(PhysicalField("1. m").log10(), 6))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
multiply(other)

Multiply two physical quantities. The unit of the result is the product of the units of the operands.

>>> print(PhysicalField(10., 'N') * PhysicalField(10., 'm') == PhysicalField(100., 'N*m'))
True

As a special case, if the result is dimensionless, the value is returned without units, rather than with a dimensionless unit of 1. This facilitates passing physical quantities to packages such as Numeric that cannot use units, while ensuring the quantities have the desired units.

>>> print((PhysicalField(10., 's') * PhysicalField(2., 'Hz')))
20.0
property numericValue

Return the PhysicalField without units, after conversion to base SI units.

>>> print(numerix.round(PhysicalField("1 inch").numericValue, 6))
0.0254
put(indices, values)

put is the opposite of take. The values of self at the locations specified in indices are set to the corresponding value of values.

The indices can be any integer sequence object with values suitable for indexing into the flat form of self. The values must be any sequence of values that can be converted to the typecode of self.

>>> f = PhysicalField((1., 2., 3.), "m")
>>> f.put((2, 0), PhysicalField((2., 3.), "inch"))
>>> print(f)
[ 0.0762  2.      0.0508] m

The units of values must be compatible with self.

>>> f.put(1, PhysicalField(3, "kg"))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
ravel()
reshape(shape)

Changes the shape of self to that specified in shape

>>> print(PhysicalField((1., 2., 3., 4.), "m").reshape((2, 2)))
[[ 1.  2.]
 [ 3.  4.]] m

The new shape must have the same size as the existing one.

>>> print(PhysicalField((1., 2., 3., 4.), "m").reshape((2, 3))) 
Traceback (most recent call last):
    ...
ValueError: total size of new array must be unchanged
property shape

Tuple of array dimensions.

sign()

Return the sign of the quantity. The unit is unchanged.

>>> from fipy.tools.numerix import sign
>>> print(sign(PhysicalField(((3., -2.), (-1., 4.)), 'm')))
[[ 1. -1.]
 [-1.  1.]]
sin()

Return the sine of the PhysicalField

>>> print(PhysicalField(numerix.pi/6, "rad").sin())
0.5
>>> print(PhysicalField(30., "deg").sin())
0.5

The units of the PhysicalField must be an angle

>>> PhysicalField(30., "m").sin()
Traceback (most recent call last):
    ...
TypeError: Incompatible units
sinh()

Return the hyperbolic sine of the PhysicalField

>>> PhysicalField(0.).sinh()
0.0

The units of the PhysicalField must be dimensionless

>>> PhysicalField(60., "m").sinh()
Traceback (most recent call last):
    ...
TypeError: Incompatible units
sqrt()

Return the square root of the PhysicalField

>>> print(PhysicalField("100. m**2").sqrt())
10.0 m

The resulting unit must be integral

>>> print(PhysicalField("100. m").sqrt())
Traceback (most recent call last):
    ...
TypeError: Illegal exponent
subtract(other)

Subtract two physical quantities, so long as their units are compatible. The unit of the result is the unit of the first operand.

>>> print(PhysicalField(10., 'km') - PhysicalField(10., 'm'))
9.99 km
>>> print(PhysicalField(10., 'km') - PhysicalField(10., 'J'))
Traceback (most recent call last):
    ...
TypeError: Incompatible units
sum(index=0)

Returns the sum of all of the elements in self along the specified axis (first axis by default).

>>> print(PhysicalField(((1., 2.), (3., 4.)), "m").sum())
[ 4.  6.] m
>>> print(PhysicalField(((1., 2.), (3., 4.)), "m").sum(1))
[ 3.  7.] m
take(indices, axis=0)

Return the elements of self specified by the elements of indices. The resulting PhysicalField array has the same units as the original.

>>> print(PhysicalField((1., 2., 3.), "m").take((2, 0)))
[ 3.  1.] m

The optional third argument specifies the axis along which the selection occurs, and the default value (as in the example above) is 0, the first axis.

>>> print(PhysicalField(((1., 2., 3.), (4., 5., 6.)), "m").take((2, 0), axis = 1))
[[ 3.  1.]
 [ 6.  4.]] m
tan()

Return the tangent of the PhysicalField

>>> numerix.round(PhysicalField(numerix.pi/4, "rad").tan(), 6)
1.0
>>> numerix.round(PhysicalField(45, "deg").tan(), 6)
1.0

The units of the PhysicalField must be an angle

>>> PhysicalField(45., "m").tan()
Traceback (most recent call last):
    ...
TypeError: Incompatible units
tanh()

Return the hyperbolic tangent of the PhysicalField

>>> print(numerix.allclose(PhysicalField(1.).tanh(), 0.761594155956))
True

The units of the PhysicalField must be dimensionless

>>> PhysicalField(60., "m").tanh()
Traceback (most recent call last):
    ...
TypeError: Incompatible units
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')

Return human-readable form of a physical quantity

>>> p = PhysicalField(value = (3., 3.14159), unit = "eV")
>>> print(p.tostring(precision = 3, separator = '|'))
[ 3.   | 3.142] eV
property unit

Return the unit object of self.

>>> PhysicalField("1 m").unit
<PhysicalUnit m>
class fipy.PowerLawConvectionTerm(coeff=1.0, var=None)

Bases: _AsymmetricConvectionTerm

The discretization for this Term is given by

\int_V \nabla \cdot (\vec{u} \phi)\,dV \simeq \sum_{f} (\vec{n} \cdot \vec{u})_f \phi_f A_f

where \phi_f=\alpha_f \phi_P +(1-\alpha_f)\phi_A and \alpha_f is calculated using the power law scheme. For further details see Numerical Schemes.

Create a _AbstractConvectionTerm object.

>>> from fipy import *
>>> m = Grid1D(nx = 2)
>>> cv = CellVariable(mesh = m)
>>> fv = FaceVariable(mesh = m)
>>> vcv = CellVariable(mesh=m, rank=1)
>>> vfv = FaceVariable(mesh=m, rank=1)
>>> __ConvectionTerm(coeff = cv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = fv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = vcv)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = vfv)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = (1,))
__ConvectionTerm(coeff=(1,))
>>> ExplicitUpwindConvectionTerm(coeff = (0,)).solve(var=cv, solver=DummySolver()) 
Traceback (most recent call last):
    ...
TransientTermError: The equation requires a TransientTerm with explicit convection.
>>> (TransientTerm(0.) - ExplicitUpwindConvectionTerm(coeff = (0,))).solve(var=cv, solver=DummySolver(), dt=1.)

>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = 1)).solve(var=cv, solver=DummySolver(), dt=1.) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> m2 = Grid2D(nx=2, ny=1)
>>> cv2 = CellVariable(mesh=m2)
>>> vcv2 = CellVariable(mesh=m2, rank=1)
>>> vfv2 = FaceVariable(mesh=m2, rank=1)
>>> __ConvectionTerm(coeff=vcv2)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> __ConvectionTerm(coeff=vfv2)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = ((0,), (0,)))).solve(var=cv2, solver=DummySolver(), dt=1.)
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = (0, 0))).solve(var=cv2, solver=DummySolver(), dt=1.)
Parameters:

coeff (The Term’s coefficient value.) –

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

exception fipy.PreconditionerNotPositiveDefiniteWarning(solver, iter, relres)

Bases: PreconditionerWarning

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

exception fipy.PreconditionerWarning(solver, iter, relres)

Bases: SolverConvergenceWarning

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class fipy.ResidualTerm(equation, underRelaxation=1.0)

Bases: _ExplicitSourceTerm

The ResidualTerm is a special form of explicit SourceTerm that adds the residual of one equation to another equation. Useful for Newton’s method.

Create a Term.

Parameters:

coeff (float or CellVariable or FaceVariable) – Coefficient for the term. FaceVariable objects are only acceptable for diffusion or convection terms.

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

exception fipy.ScalarQuantityOutOfRangeWarning(solver, iter, relres)

Bases: SolverConvergenceWarning

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class fipy.ScharfetterGummelFaceVariable(*args, **kwds)

Bases: _CellToFaceVariable

Parameters:

  • mesh (Mesh) – the mesh that defines the geometry of this Variable

  • name (str) – the user-readable name of the Variable

  • value (float or array_like) – the initial value

  • rank (int) – the rank (number of dimensions) of each element of this Variable. Default: 0

  • elementshape (tuple of int) – the shape of each element of this variable Default: rank * (mesh.dim,)

  • unit (str or PhysicalUnit) – The physical units of the variable

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__()

“Evaluate” the Variable and return its value

>>> a = Variable(value=3)
>>> print(a())
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b()
7
__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the _MeshVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new Variable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
cacheMe(recursive=False)
constrain(value, where=None)

Constrain the Variable to have a value at an index or mask location specified by where.

>>> v = Variable((0, 1, 2, 3))
>>> v.constrain(2, numerix.array((True, False, False, False)))
>>> print(v)
[2 1 2 3]
>>> v[:] = 10
>>> print(v)
[ 2 10 10 10]
>>> v.constrain(5, numerix.array((False, False, True, False)))
>>> print(v)
[ 2 10  5 10]
>>> v[:] = 6
>>> print(v)
[2 6 5 6]
>>> v.constrain(8)
>>> print(v)
[8 8 8 8]
>>> v[:] = 10
>>> print(v)
[8 8 8 8]
>>> del v.constraints[2]
>>> print(v)
[ 2 10  5 10]

>>> from fipy.variables.cellVariable import CellVariable
>>> from fipy.meshes import Grid2D
>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v = CellVariable(mesh=m, rank=1, value=(x, y))
>>> v.constrain(((0.,), (-1.,)), where=m.facesLeft)
>>> print(v.faceValue)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.   1.   1.5  0.   1.   1.5]
 [ 0.5  0.5  1.   1.   1.5  1.5 -1.   0.5  0.5 -1.   1.5  1.5]]
Parameters:

  • value (float or array_like) – The value of the constraint

  • where (array_like of bool) – The constraint mask or index specifying the location of the constraint

property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Make an duplicate of the Variable

>>> a = Variable(value=3)
>>> b = a.copy()
>>> b
Variable(value=array(3))

The duplicate will not reflect changes made to the original

>>> a.setValue(5)
>>> b
Variable(value=array(3))

Check that this works for arrays.

>>> a = Variable(value=numerix.array((0, 1, 2)))
>>> b = a.copy()
>>> b
Variable(value=array([0, 1, 2]))
>>> a[1] = 3
>>> b
Variable(value=array([0, 1, 2]))
property divergence

the divergence of self, \vec{u},

\nabla\cdot\vec{u} \approx \frac{\sum_f (\vec{u}\cdot\hat{n})_f A_f}{V_P}

Returns:

divergence – one rank lower than self

Return type:

fipy.variables.cellVariable.CellVariable

Examples

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=3, ny=2)
>>> from builtins import range
>>> var = CellVariable(mesh=mesh, value=list(range(3*2)))
>>> print(var.faceGrad.divergence)
[ 4.  3.  2. -2. -3. -4.]
dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property name
property numericValue
put(indices, value)
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> c0 = Constraint(0., where=m.facesLeft)
>>> v.constrain(c0)
>>> c1 = Constraint(3., where=m.facesRight)
>>> v.faceValue.constrain(c1)
>>> print(v.faceValue)
[ 0.  1.  2.  3.]
>>> v.faceValue.release(constraint=c0)
>>> print(v.faceValue)
[ 0.5  1.   2.   3. ]
>>> v.faceValue.release(constraint=c1)
>>> print(v.faceValue)
[ 0.5  1.   2.   2.5]
setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
fipy.SharedTemporaryFile(mode='w+b', buffering=-1, encoding=None, newline=None, suffix='', prefix='tmp', dir=None, delete=True, communicator=SerialPETScCommWrapper())

Create a temporary file shared by all MPI ranks.

The file is created as NamedTemporaryFile would do it. The name of the returned file-like object is accessible as its name attribute. The file will be automatically deleted when it is closed unless the delete argument is set to False.

>>> from fipy.tools import SharedTemporaryFile, parallelComm
>>> with SharedTemporaryFile(mode='w+', suffix=".tmp") as tmpFile:
...     # write on processor 0
...     if parallelComm.procID == 0:
...         _ = tmpFile.write("shared text")
...
...     parallelComm.Barrier()
...
...     # read on all processors
...     _ = tmpFile.seek(0)
...     txt = tmpFile.read()
>>> print(txt)
shared text
Parameters:

  • prefix (str) – As for mkstemp

  • suffix (str) – As for mkstemp

  • dir (str) – As for mkstemp

  • mode (str) – The mode argument to io.open (default “w+b”)

  • buffering (int) – The buffer size argument to io.open (default -1)

  • encoding (str or None) – The encoding argument to io.open (default None)

  • newline (str or None) – The newline argument to io.open (default None)

  • delete (bool) – Whether the file is deleted on close (default True)

  • communicator (CommWrapper) – MPI communicator describing ranks to share with. A duck-typed object with procID and Nproc attributes is sufficient.

Return type:

file-like object

See also

tempfile.NamedTemporaryFile, tempfile.mkstemp, io.open

class fipy.SkewedGrid2D(dx=1.0, dy=1.0, nx=None, ny=1, rand=0, *args, **kwargs)

Bases: Mesh2D

Creates a 2D grid mesh with horizontal faces numbered first and then vertical faces. The points are skewed by a random amount (between rand and -rand) in the X and Y directions.

Note

This Mesh only operates in serial

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
extrude(extrudeFunc=<function Mesh2D.<lambda>>, layers=1)

This function returns a new 3D mesh. The 2D mesh is extruded using the extrudeFunc and the number of layers.

>>> from fipy.meshes.nonUniformGrid2D import NonUniformGrid2D
>>> print(NonUniformGrid2D(nx=2, ny=2).extrude(layers=2).cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]]

>>> from fipy.meshes.tri2D import Tri2D
>>> print(Tri2D().extrude(layers=2).cellCenters.allclose([[ 0.83333333, 0.5,        0.16666667, 0.5,       0.83333333, 0.5,
...                                                      0.16666667, 0.5       ],
...                                                       [ 0.5,        0.83333333, 0.5,        0.16666667, 0.5,        0.83333333,
...                                                      0.5,        0.16666667],
...                                                      [ 0.5,        0.5,        0.5,        0.5,        1.5,        1.5,        1.5,
...                                                      1.5       ]]))
True
Parameters:

  • extrudeFunc (function) – Takes the vertex coordinates and returns the displaced values

  • layers (int) – Number of layers in the extruded mesh (number of times extrudeFunc will be called)

property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property physicalShape

Return physical dimensions of Grid2D.

property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property shape
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
exception fipy.SolutionVariableNumberError(s='Different number of solution variables and equations.')

Bases: Exception

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

exception fipy.SolutionVariableRequiredError(s='The solution variable needs to be specified.')

Bases: Exception

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class fipy.Solver(tolerance=1e-10, iterations=1000, precon=None)

Bases: object

The base LinearXSolver class.

Attention

This class is abstract. Always create one of its subclasses.

Create a Solver object.

Parameters:

  • tolerance (float) – Required error tolerance.

  • iterations (int) – Maximum number of iterative steps to perform.

  • precon – Preconditioner to use. Not all solver suites support preconditioners.

__del__()
__enter__()
__exit__(exc_type, exc_value, traceback)
__repr__()

Return repr(self).

exception fipy.SolverConvergenceWarning(solver, iter, relres)

Bases: Warning

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

fipy.SphericalGrid1D(dr=None, nr=None, Lr=None, dx=1.0, nx=None, Lx=None, origin=(0,), overlap=2, communicator=SerialPETScCommWrapper())

Create a 1D spherical mesh

Factory function to select between SphericalUniformGrid1D and SphericalNonUniformGrid1D. If Lr is specified the length of the domain is always Lr regardless of dr, unless dr is a list of spacings, in which case Lr will be the sum of dr.

Parameters:

  • dr (float) – Grid spacing in the radial direction. Alternative: dx.

  • nr (int) – Number of cells in the radial direction. Alternative: nx.

  • Lr (float) – Domain length in the radial direction. Alternative: Lx.

  • overlap (int) – the number of overlapping cells for parallel simulations. Generally 2 is adequate. Higher order equations or discretizations require more.

  • communicator (CommWrapper) – MPI communicator to use. Select serialComm to create a serial mesh when running in parallel; mostly used for test purposes. (default: parallelComm).

exception fipy.StagnatedSolverWarning(solver, iter, relres)

Bases: SolverConvergenceWarning

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class fipy.SurfactantConvectionVariable(*args, **kwds)

Bases: FaceVariable

Convection coefficient for the ConservativeSurfactantEquation. The coefficient only has a value for a negative distanceVar.

Simple one dimensional test:

>>> from fipy.variables.cellVariable import CellVariable
>>> from fipy.meshes import Grid2D
>>> mesh = Grid2D(nx = 3, ny = 1, dx = 1., dy = 1.)
>>> from fipy.variables.distanceVariable import DistanceVariable
>>> distanceVar = DistanceVariable(mesh, value = (-.5, .5, 1.5))
>>> ## answer = numerix.zeros((2, mesh.numberOfFaces),'d')
>>> answer = FaceVariable(mesh=mesh, rank=1, value=0.).globalValue
>>> answer[0, 7] = -1
>>> print(numerix.allclose(SurfactantConvectionVariable(distanceVar).globalValue, answer))
True

Change the dimensions:

>>> mesh = Grid2D(nx = 3, ny = 1, dx = .5, dy = .25)
>>> distanceVar = DistanceVariable(mesh, value = (-.25, .25, .75))
>>> answer[0, 7] = -.5
>>> print(numerix.allclose(SurfactantConvectionVariable(distanceVar).globalValue, answer))
True

Two dimensional example:

>>> mesh = Grid2D(nx = 2, ny = 2, dx = 1., dy = 1.)
>>> distanceVar = DistanceVariable(mesh, value = (-1.5, -.5, -.5, .5))
 >>> answer = FaceVariable(mesh=mesh, rank=1, value=0.).globalValue
>>> answer[1, 2] = -.5
>>> answer[1, 3] = -1
>>> answer[0, 7] = -.5
>>> answer[0, 10] = -1
>>> print(numerix.allclose(SurfactantConvectionVariable(distanceVar).globalValue, answer))
True

Larger grid:

>>> mesh = Grid2D(nx = 3, ny = 3, dx = 1., dy = 1.)
>>> distanceVar = DistanceVariable(mesh, value = (1.5, .5, 1.5,
...                                           .5, -.5, .5,
...                                           1.5, .5, 1.5))
 >>> answer = FaceVariable(mesh=mesh, rank=1, value=0.).globalValue
>>> answer[1, 4] = .25
>>> answer[1, 7] = -.25
>>> answer[0, 17] = .25
>>> answer[0, 18] = -.25
>>> print(numerix.allclose(SurfactantConvectionVariable(distanceVar).globalValue, answer))
True
__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__()

“Evaluate” the Variable and return its value

>>> a = Variable(value=3)
>>> print(a())
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b()
7
__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the _MeshVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new Variable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
cacheMe(recursive=False)
constrain(value, where=None)

Constrain the Variable to have a value at an index or mask location specified by where.

>>> v = Variable((0, 1, 2, 3))
>>> v.constrain(2, numerix.array((True, False, False, False)))
>>> print(v)
[2 1 2 3]
>>> v[:] = 10
>>> print(v)
[ 2 10 10 10]
>>> v.constrain(5, numerix.array((False, False, True, False)))
>>> print(v)
[ 2 10  5 10]
>>> v[:] = 6
>>> print(v)
[2 6 5 6]
>>> v.constrain(8)
>>> print(v)
[8 8 8 8]
>>> v[:] = 10
>>> print(v)
[8 8 8 8]
>>> del v.constraints[2]
>>> print(v)
[ 2 10  5 10]

>>> from fipy.variables.cellVariable import CellVariable
>>> from fipy.meshes import Grid2D
>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v = CellVariable(mesh=m, rank=1, value=(x, y))
>>> v.constrain(((0.,), (-1.,)), where=m.facesLeft)
>>> print(v.faceValue)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.   1.   1.5  0.   1.   1.5]
 [ 0.5  0.5  1.   1.   1.5  1.5 -1.   0.5  0.5 -1.   1.5  1.5]]
Parameters:

  • value (float or array_like) – The value of the constraint

  • where (array_like of bool) – The constraint mask or index specifying the location of the constraint

property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Make an duplicate of the Variable

>>> a = Variable(value=3)
>>> b = a.copy()
>>> b
Variable(value=array(3))

The duplicate will not reflect changes made to the original

>>> a.setValue(5)
>>> b
Variable(value=array(3))

Check that this works for arrays.

>>> a = Variable(value=numerix.array((0, 1, 2)))
>>> b = a.copy()
>>> b
Variable(value=array([0, 1, 2]))
>>> a[1] = 3
>>> b
Variable(value=array([0, 1, 2]))
property divergence

the divergence of self, \vec{u},

\nabla\cdot\vec{u} \approx \frac{\sum_f (\vec{u}\cdot\hat{n})_f A_f}{V_P}

Returns:

divergence – one rank lower than self

Return type:

fipy.variables.cellVariable.CellVariable

Examples

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=3, ny=2)
>>> from builtins import range
>>> var = CellVariable(mesh=mesh, value=list(range(3*2)))
>>> print(var.faceGrad.divergence)
[ 4.  3.  2. -2. -3. -4.]
dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property name
property numericValue
put(indices, value)
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> v = Variable((0, 1, 2, 3))
>>> v.constrain(2, numerix.array((True, False, False, False)))
>>> v[:] = 10
>>> from fipy.boundaryConditions.constraint import Constraint
>>> c1 = Constraint(5, numerix.array((False, False, True, False)))
>>> v.constrain(c1)
>>> v[:] = 6
>>> v.constrain(8)
>>> v[:] = 10
>>> del v.constraints[2]
>>> v.release(constraint=c1)
>>> print(v)
[ 2 10 10 10]
setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
class fipy.SurfactantVariable(*args, **kwds)

Bases: CellVariable

The SurfactantVariable maintains a conserved volumetric concentration on cells adjacent to, but in front of, the interface. The value argument corresponds to the initial concentration of surfactant on the interface (moles divided by area). The value held by the SurfactantVariable is actually a volume density (moles divided by volume).

A simple 1D test:

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(dx = 1., nx = 4)
>>> from fipy.variables.distanceVariable import DistanceVariable
>>> distanceVariable = DistanceVariable(mesh = mesh,
...                                     value = (-1.5, -0.5, 0.5, 941.5))
>>> surfactantVariable = SurfactantVariable(value = 1,
...                                         distanceVar = distanceVariable)
>>> print(numerix.allclose(surfactantVariable, (0, 0., 1., 0)))
1

A 2D test case:

>>> from fipy.meshes import Grid2D
>>> mesh = Grid2D(dx = 1., dy = 1., nx = 3, ny = 3)
>>> distanceVariable = DistanceVariable(mesh = mesh,
...                                     value = (1.5, 0.5, 1.5,
...                                              0.5, -0.5, 0.5,
...                                              1.5, 0.5, 1.5))
>>> surfactantVariable = SurfactantVariable(value = 1,
...                                         distanceVar = distanceVariable)
>>> print(numerix.allclose(surfactantVariable, (0, 1, 0, 1, 0, 1, 0, 1, 0)))
1

Another 2D test case:

>>> mesh = Grid2D(dx = .5, dy = .5, nx = 2, ny = 2)
>>> distanceVariable = DistanceVariable(mesh = mesh,
...                                     value = (-0.5, 0.5, 0.5, 1.5))
>>> surfactantVariable = SurfactantVariable(value = 1,
...                                         distanceVar = distanceVariable)
>>> print(numerix.allclose(surfactantVariable,
...                  (0, numerix.sqrt(2), numerix.sqrt(2), 0)))
1
Parameters:

  • value (float or array_like) – The initial value.

  • distanceVar (DistanceVariable) –

  • name (str) – The name of the variable.

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__(points=None, order=0, nearestCellIDs=None)

Interpolates the CellVariable to a set of points using a method that has a memory requirement on the order of Ncells by Npoints in general, but uses only Ncells when the CellVariable’s mesh is a UniformGrid object.

Tests

>>> from fipy import *
>>> m = Grid2D(nx=3, ny=2)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.))))
[ 0.5  1.5  1.5]
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.)), order=1))
[ 0.25  1.1   1.2 ]
>>> m0 = Grid2D(nx=2, ny=2, dx=1., dy=1.)
>>> m1 = Grid2D(nx=4, ny=4, dx=.5, dy=.5)
>>> x, y = m0.cellCenters
>>> v0 = CellVariable(mesh=m0, value=x * y)
>>> print(v0(m1.cellCenters.globalValue))
[ 0.25  0.25  0.75  0.75  0.25  0.25  0.75  0.75  0.75  0.75  2.25  2.25
  0.75  0.75  2.25  2.25]
>>> print(v0(m1.cellCenters.globalValue, order=1))
[ 0.125  0.25   0.5    0.625  0.25   0.375  0.875  1.     0.5    0.875
  1.875  2.25   0.625  1.     2.25   2.625]
Parameters:

  • points (tuple or list of tuple) – A point or set of points in the format (X, Y, Z)

  • order ({0, 1}) – The order of interpolation, default is 0

  • nearestCellIDs (array_like) – Optional argument if user can calculate own nearest cell IDs array, shape should be same as points

__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the CellVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new CellVariable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
property arithmeticFaceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
cacheMe(recursive=False)
property cellVolumeAverage

Return the cell-volume-weighted average of the CellVariable:

<\phi>_\text{vol} = \frac{\sum_\text{cells} \phi_\text{cell} V_\text{cell}} {\sum_\text{cells} V_\text{cell}}

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx = 3, ny = 1, dx = .5, dy = .1)
>>> var = CellVariable(value = (1, 2, 6), mesh = mesh)
>>> print(var.cellVolumeAverage)
3.0
constrain(value, where=None)

Constrains the CellVariable to value at a location specified by where.

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> v.constrain(0., where=m.facesLeft)
>>> v.faceGrad.constrain([1.], where=m.facesRight)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]

Changing the constraint changes the dependencies

>>> v.constrain(1., where=m.facesLeft)
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can be Variable

>>> c = Variable(0.)
>>> v.constrain(c, where=m.facesLeft)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> c.value = 1.
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can have a Variable mask.

>>> v = CellVariable(mesh=m)
>>> mask = FaceVariable(mesh=m, value=m.facesLeft)
>>> v.constrain(1., where=mask)
>>> print(v.faceValue)
[ 1.  0.  0.  0.]
>>> mask[:] = mask | m.facesRight
>>> print(v.faceValue)
[ 1.  0.  0.  1.]
property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Make an duplicate of the Variable

>>> a = Variable(value=3)
>>> b = a.copy()
>>> b
Variable(value=array(3))

The duplicate will not reflect changes made to the original

>>> a.setValue(5)
>>> b
Variable(value=array(3))

Check that this works for arrays.

>>> a = Variable(value=numerix.array((0, 1, 2)))
>>> b = a.copy()
>>> b
Variable(value=array([0, 1, 2]))
>>> a[1] = 3
>>> b
Variable(value=array([0, 1, 2]))
dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

property faceGrad

Return \nabla \phi as a rank-1 FaceVariable using differencing for the normal direction(second-order gradient).

property faceGradAverage

Deprecated since version 3.3: use grad.arithmeticFaceValue() instead

Return \nabla \phi as a rank-1 FaceVariable using averaging for the normal direction(second-order gradient)

property faceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
property gaussGrad

Return \frac{1}{V_P} \sum_f \vec{n} \phi_f A_f as a rank-1 CellVariable (first-order gradient).

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue

Concatenate and return values from all processors

When running on a single processor, the result is identical to value.

property grad

Return \nabla \phi as a rank-1 CellVariable (first-order gradient).

property harmonicFaceValue

Returns a FaceVariable whose value corresponds to the harmonic interpolation of the adjacent cells:

\phi_f = \frac{\phi_1 \phi_2}{(\phi_2 - \phi_1) \frac{d_{f2}}{d_{12}} + \phi_1}

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (0.5 / 1.) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (1.0 / 3.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (5.0 / 55.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
property interfaceVar

Returns the SurfactantVariable rendered as an _InterfaceSurfactantVariable which evaluates the surfactant concentration as an area concentration the interface rather than a volumetric concentration.

itemset(value)
property itemsize
property leastSquaresGrad

Return \nabla \phi, which is determined by solving for \nabla \phi in the following matrix equation,

\nabla \phi \cdot \sum_f d_{AP}^2 \vec{n}_{AP} \otimes \vec{n}_{AP} = \sum_f d_{AP}^2 \left( \vec{n} \cdot \nabla \phi \right)_{AP}

The matrix equation is derived by minimizing the following least squares sum,

F \left( \phi_x, \phi_y \right) = \sqrt{\sum_f \left( d_{AP} \vec{n}_{AP} \cdot \nabla \phi - d_{AP} \left( \vec{n}_{AP} \cdot \nabla \phi \right)_{AP} \right)^2 }

Tests

>>> from fipy import Grid2D
>>> m = Grid2D(nx=2, ny=2, dx=0.1, dy=2.0)
>>> print(numerix.allclose(CellVariable(mesh=m, value=(0, 1, 3, 6)).leastSquaresGrad.globalValue, \
...                                     [[8.0, 8.0, 24.0, 24.0],
...                                      [1.2, 2.0, 1.2, 2.0]]))
True

>>> from fipy import Grid1D
>>> print(numerix.allclose(CellVariable(mesh=Grid1D(dx=(2.0, 1.0, 0.5)),
...                                     value=(0, 1, 2)).leastSquaresGrad.globalValue, [[0.461538461538, 0.8, 1.2]]))
True
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property minmodFaceValue

Returns a FaceVariable with a value that is the minimum of the absolute values of the adjacent cells. If the values are of opposite sign then the result is zero:

\phi_f = \begin{cases} \phi_1& \text{when $|\phi_1| \le |\phi_2|$},\\ \phi_2& \text{when $|\phi_2| < |\phi_1|$},\\ 0 & \text{when $\phi1 \phi2 < 0$} \end{cases}

>>> from fipy import *
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(1, 2)).minmodFaceValue)
[1 1 2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, -2)).minmodFaceValue)
[-1 -1 -2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, 2)).minmodFaceValue)
[-1  0  2]
property name
property numericValue
property old

Return the values of the CellVariable from the previous solution sweep.

Combinations of CellVariable’s should also return old values.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 2)
>>> from fipy.variables.cellVariable import CellVariable
>>> var1 = CellVariable(mesh = mesh, value = (2, 3), hasOld = 1)
>>> var2 = CellVariable(mesh = mesh, value = (3, 4))
>>> v = var1 * var2
>>> print(v)
[ 6 12]
>>> var1.value = ((3, 2))
>>> print(v)
[9 8]
>>> print(v.old)
[ 6 12]

The following small test is to correct for a bug when the operator does not just use variables.

>>> v1 = var1 * 3
>>> print(v1)
[9 6]
>>> print(v1.old)
[6 9]
put(indices, value)
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> c = Constraint(0., where=m.facesLeft)
>>> v.constrain(c)
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> v.release(constraint=c)
>>> print(v.faceValue)
[ 0.5  1.   2.   2.5]
setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
updateOld()

Set the values of the previous solution sweep to the current values.

>>> from fipy import *
>>> v = CellVariable(mesh=Grid1D(), hasOld=False)
>>> v.updateOld()
Traceback (most recent call last):
   ...
AssertionError: The updateOld method requires the CellVariable to have an old value. Set hasOld to True when instantiating the CellVariable.
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
class fipy.TSVViewer(vars, title=None, limits={}, **kwlimits)

Bases: AbstractViewer

“Views” one or more variables in tab-separated-value format.

Output is a list of coordinates and variable values at each cell center.

File contents will be, e.g.:

title
x       y       ...     var0    var2    ...
0.0     0.0     ...     3.14    1.41    ...
1.0     0.0     ...     2.72    0.866   ...
:
:

Creates a TSVViewer.

Any cell centers that lie outside the limits provided will not be included. Any values that lie outside the datamin or datamax will be replaced with nan.

All variables must have the same mesh.

It tries to do something reasonable with rank-1 CellVariable and FaceVariable objects.

Parameters:

  • vars (CellVariable or FaceVariable or list) – the MeshVariable objects to display.

  • title (str, optional) – displayed at the top of the Viewer window

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

property limits
plot(filename=None)

“plot” the coordinates and values of the variables to filename. If filename is not provided, “plots” to stdout.

>>> from fipy.meshes import Grid1D
>>> m = Grid1D(nx = 3, dx = 0.4)
>>> from fipy.variables.cellVariable import CellVariable
>>> v = CellVariable(mesh = m, name = "var", value = (0, 2, 5))
>>> TSVViewer(vars = (v, v.grad)).plot() 
x       var     var_gauss_grad_x
0.2     0       2.5
0.6     2       6.25
1       5       3.75

>>> from fipy.meshes import Grid2D
>>> m = Grid2D(nx = 2, dx = .1, ny = 2, dy = 0.3)
>>> v = CellVariable(mesh = m, name = "var", value = (0, 2, -2, 5))
>>> TSVViewer(vars = (v, v.grad)).plot() 
x       y       var     var_gauss_grad_x        var_gauss_grad_y
0.05    0.15    0       10      -3.33333333333333
0.15    0.15    2       10      5
0.05    0.45    -2      35      -3.33333333333333
0.15    0.45    5       35      5
Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

exception fipy.TermMultiplyError(s='Must multiply terms by int or float.')

Bases: Exception

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class fipy.TransientTerm(coeff=1.0, var=None)

Bases: CellTerm

The TransientTerm represents

\int_V \frac{\partial (\rho \phi)}{\partial t} dV \simeq \frac{(\rho_{P} \phi_{P} - \rho_{P}^\text{old} \phi_P^\text{old}) V_P}{\Delta t}

where \rho is the coeff value.

The following test case verifies that variable coefficients and old coefficient values work correctly. We will solve the following equation

\frac{ \partial \phi^2 } { \partial t } = k.

The analytic solution is given by

\phi = \sqrt{ \phi_0^2 + k t },

where \phi_0 is the initial value.

>>> phi0 = 1.
>>> k = 1.
>>> dt = 1.
>>> relaxationFactor = 1.5
>>> steps = 2
>>> sweeps = 8

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 1)
>>> from fipy.variables.cellVariable import CellVariable
>>> var = CellVariable(mesh = mesh, value = phi0, hasOld = 1)
>>> from fipy.terms.transientTerm import TransientTerm
>>> from fipy.terms.implicitSourceTerm import ImplicitSourceTerm

Relaxation, given by relaxationFactor, is required for a converged solution.

>>> eq = TransientTerm(var) == ImplicitSourceTerm(-relaxationFactor) \
...                            + var * relaxationFactor + k

A number of sweeps at each time step are required to let the relaxation take effect.

>>> from builtins import range
>>> for step in range(steps):
...     var.updateOld()
...     for sweep in range(sweeps):
...         eq.solve(var, dt = dt)

Compare the final result with the analytical solution.

>>> from fipy.tools import numerix
>>> print(var.allclose(numerix.sqrt(k * dt * steps + phi0**2)))
1

Create a Term.

Parameters:

coeff (float or CellVariable or FaceVariable) – Coefficient for the term. FaceVariable objects are only acceptable for diffusion or convection terms.

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

class fipy.Tri2D(dx=1.0, dy=1.0, nx=1, ny=1, _RepresentationClass=<class 'fipy.meshes.representations.gridRepresentation._Grid2DRepresentation'>, _TopologyClass=<class 'fipy.meshes.topologies.meshTopology._Mesh2DTopology'>)

Bases: Mesh2D

This class creates a mesh made out of triangles. It does this by starting with a standard Cartesian mesh (Grid2D) and dividing each cell in that mesh (hereafter referred to as a “box”) into four equal parts with the dividing lines being the diagonals.

Creates a 2D triangular mesh with horizontal faces numbered first then vertical faces, then diagonal faces. Vertices are numbered starting with the vertices at the corners of boxes and then the vertices at the centers of boxes. Cells on the right of boxes are numbered first, then cells on the top of boxes, then cells on the left of boxes, then cells on the bottom of boxes. Within each of the “sub-categories” in the above, the vertices, cells and faces are numbered in the usual way.

Parameters:

  • dx (float) – The X and Y dimensions of each “box”. If dx <> dy, the line segments connecting the cell centers will not be orthogonal to the faces.

  • dy (float) – The X and Y dimensions of each “box”. If dx <> dy, the line segments connecting the cell centers will not be orthogonal to the faces.

  • nx (int) – The number of boxes in the X direction and the Y direction. The total number of boxes will be equal to nx * ny, and the total number of cells will be equal to 4 * nx * ny.

  • ny (int) – The number of boxes in the X direction and the Y direction. The total number of boxes will be equal to nx * ny, and the total number of cells will be equal to 4 * nx * ny.

property VTKCellDataSet

Returns a TVTK DataSet representing the cells of this mesh

property VTKFaceDataSet

Returns a TVTK DataSet representing the face centers of this mesh

__add__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__div__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

__getstate__()

Helper for pickle.

__mul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__radd__(other)

Either translate a Mesh or concatenate two Mesh objects.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

If a vector is added to a Mesh, a translated Mesh is returned

>>> translatedMesh = baseMesh + ((5,), (10,))
>>> print(translatedMesh.cellCenters)
[[  5.5   6.5   5.5   6.5]
 [ 10.5  10.5  11.5  11.5]]

If a Mesh is added to a Mesh, a concatenation of the two Mesh objects is returned

>>> addedMesh = baseMesh + (baseMesh + ((2,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

The two Mesh objects need not be properly aligned in order to concatenate them but the resulting mesh may not have the intended connectivity

>>> addedMesh = baseMesh + (baseMesh + ((3,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  3.5  4.5  3.5  4.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

>>> addedMesh = baseMesh + (baseMesh + ((2,), (2,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  2.5  3.5  2.5  3.5]
 [ 0.5  0.5  1.5  1.5  2.5  2.5  3.5  3.5]]

No provision is made to avoid or consolidate overlapping Mesh objects

>>> addedMesh = baseMesh + (baseMesh + ((1,), (0,)))
>>> print(addedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  1.5  2.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]]

Different Mesh classes can be concatenated

>>> from fipy.meshes import Tri2D
>>> triMesh = Tri2D(dx = 1.0, dy = 1.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[0.5, 1.5, 0.5, 1.5, 2.83333333,  3.83333333,
...                 2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [0.5, 0.5, 1.5, 1.5, 0.5, 0.5, 0.83333333, 0.83333333,
...                 0.5, 0.5, 0.16666667, 0.16666667]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

again, their faces need not align, but the mesh may not have the desired connectivity

>>> triMesh = Tri2D(dx = 1.0, dy = 2.0, nx = 2, ny = 1)
>>> triMesh = triMesh + ((2,), (0,))
>>> triAddedMesh = baseMesh + triMesh
>>> cellCenters = [[ 0.5, 1.5, 0.5, 1.5, 2.83333333, 3.83333333,
...                  2.5, 3.5, 2.16666667, 3.16666667, 2.5, 3.5],
...                [ 0.5, 0.5, 1.5, 1.5, 1., 1.,
...                  1.66666667, 1.66666667, 1., 1., 0.33333333, 0.33333333]]
>>> print(numerix.allclose(triAddedMesh.cellCenters,
...                        cellCenters))
True

Mesh concatenation is not limited to 2D meshes

>>> from fipy.meshes import Grid3D
>>> threeDBaseMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                         nx = 2, ny = 2, nz = 2)
>>> threeDSecondMesh = Grid3D(dx = 1.0, dy = 1.0, dz = 1.0,
...                           nx = 1, ny = 1, nz = 1)
>>> threeDAddedMesh = threeDBaseMesh + (threeDSecondMesh + ((2,), (0,), (0,)))
>>> print(threeDAddedMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5  2.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5  0.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5  0.5]]

but the different Mesh objects must, of course, have the same dimensionality.

>>> InvalidMesh = threeDBaseMesh + baseMesh 
Traceback (most recent call last):
...
MeshAdditionError: Dimensions do not match
__repr__()

Return repr(self).

__rmul__(factor)

Dilate a Mesh by factor.

>>> from fipy.meshes import Grid2D
>>> baseMesh = Grid2D(dx = 1.0, dy = 1.0, nx = 2, ny = 2)
>>> print(baseMesh.cellCenters)
[[ 0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5]]

The factor can be a scalar

>>> dilatedMesh = baseMesh * 3
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.5  1.5  4.5  4.5]]

or a vector

>>> dilatedMesh = baseMesh * ((3,), (2,))
>>> print(dilatedMesh.cellCenters)
[[ 1.5  4.5  1.5  4.5]
 [ 1.   1.   3.   3. ]]

but the vector must have the same dimensionality as the Mesh

>>> dilatedMesh = baseMesh * ((3,), (2,), (1,)) 
Traceback (most recent call last):
...
ValueError: shape mismatch: objects cannot be broadcast to a single shape
__setstate__(state)
__sub__(other)

Tests. >>> from fipy import * >>> m = Grid1D() >>> print((m - ((1,))).cellCenters) [[-0.5]] >>> ((1,)) - m Traceback (most recent call last): … TypeError: unsupported operand type(s) for -: ‘tuple’ and ‘UniformGrid1D’

__truediv__(other)

Tests. >>> from fipy import * >>> print((Grid1D(nx=1) / 2.).cellCenters) [[ 0.25]] >>> AbstractMesh(communicator=None) / 2. Traceback (most recent call last): … NotImplementedError

property aspect2D

The physical y vs x aspect ratio of a 2D mesh

property cellCenters
property cellDistanceVectors
property cellFaceIDs
property cellToFaceDistanceVectors
property cellVolumes
property extents
property exteriorFaces
extrude(extrudeFunc=<function Mesh2D.<lambda>>, layers=1)

This function returns a new 3D mesh. The 2D mesh is extruded using the extrudeFunc and the number of layers.

>>> from fipy.meshes.nonUniformGrid2D import NonUniformGrid2D
>>> print(NonUniformGrid2D(nx=2, ny=2).extrude(layers=2).cellCenters)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.5  1.5]
 [ 0.5  0.5  1.5  1.5  0.5  0.5  1.5  1.5]
 [ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]]

>>> from fipy.meshes.tri2D import Tri2D
>>> print(Tri2D().extrude(layers=2).cellCenters.allclose([[ 0.83333333, 0.5,        0.16666667, 0.5,       0.83333333, 0.5,
...                                                      0.16666667, 0.5       ],
...                                                       [ 0.5,        0.83333333, 0.5,        0.16666667, 0.5,        0.83333333,
...                                                      0.5,        0.16666667],
...                                                      [ 0.5,        0.5,        0.5,        0.5,        1.5,        1.5,        1.5,
...                                                      1.5       ]]))
True
Parameters:

  • extrudeFunc (function) – Takes the vertex coordinates and returns the displaced values

  • layers (int) – Number of layers in the extruded mesh (number of times extrudeFunc will be called)

property faceCenters
property facesBack

Return list of faces on back boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((6, 7, 8, 9, 10, 11),
...                        numerix.nonzero(mesh.facesBack)[0])) 
True
>>> ignore = mesh.facesBack.value
property facesBottom

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesDown

Return list of faces on bottom boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((12, 13, 14),
...                        numerix.nonzero(mesh.facesBottom)[0])) 
True
>>> ignore = mesh.facesBottom.value 
>>> x, y, z = mesh.faceCenters
>>> print(numerix.allequal((12, 13),
...                        numerix.nonzero(mesh.facesBottom & (x < 1))[0])) 
True
>>> ignore = mesh.facesBottom.value
property facesFront

Return list of faces on front boundary of 3D Mesh with the z-axis running from front to back.

>>> from fipy import Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((0, 1, 2, 3, 4, 5),
...                        numerix.nonzero(mesh.facesFront)[0])) 
True
>>> ignore = mesh.facesFront.value
property facesLeft

Return face on left boundary of Mesh as list with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((21, 25),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((9, 13),
...                        numerix.nonzero(mesh.facesLeft)[0])) 
True
>>> ignore = mesh.facesLeft.value
property facesRight

Return list of faces on right boundary of Mesh with the x-axis running from left to right.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((24, 28),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((12, 16),
...                        numerix.nonzero(mesh.facesRight)[0])) 
True
>>> ignore = mesh.facesRight.value
property facesTop

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
property facesUp

Return list of faces on top boundary of 2D or 3D Mesh with the y-axis running from bottom to top.

>>> from fipy import Grid2D, Grid3D, numerix
>>> mesh = Grid3D(nx = 3, ny = 2, nz = 1, dx = 0.5, dy = 2., dz = 4.)
>>> print(numerix.allequal((18, 19, 20),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value 
>>> mesh = Grid2D(nx = 3, ny = 2, dx = 0.5, dy = 2.)
>>> print(numerix.allequal((6, 7, 8),
...                        numerix.nonzero(mesh.facesTop)[0])) 
True
>>> ignore = mesh.facesTop.value
getNearestCell(point)
property interiorFaceCellIDs
property interiorFaceIDs
property interiorFaces
property physicalShape

Return physical dimensions of Grid2D.

property scale
property scaledCellDistances
property scaledCellToCellDistances
property scaledCellVolumes
property scaledFaceAreas
property scaledFaceToCellDistances
property shape
property x

Equivalent to using cellCenters[0].

>>> from fipy import *
>>> print(Grid1D(nx=2).x)
[ 0.5  1.5]
property y

Equivalent to using cellCenters[1].

>>> from fipy import *
>>> print(Grid2D(nx=2, ny=2).y)
[ 0.5  0.5  1.5  1.5]
>>> print(Grid1D(nx=2).y)
Traceback (most recent call last):
  ...
AttributeError: 1D meshes do not have a "y" attribute.
property z

Equivalent to using cellCenters[2].

>>> from fipy import *
>>> print(Grid3D(nx=2, ny=2, nz=2).z)
[ 0.5  0.5  0.5  0.5  1.5  1.5  1.5  1.5]
>>> print(Grid2D(nx=2, ny=2).z)
Traceback (most recent call last):
  ...
AttributeError: 1D and 2D meshes do not have a "z" attribute.
class fipy.UniformNoiseVariable(*args, **kwds)

Bases: NoiseVariable

Represents a uniform distribution of random numbers.

We generate noise on a uniform Cartesian mesh

>>> from fipy.meshes import Grid2D
>>> noise = UniformNoiseVariable(mesh=Grid2D(nx=100, ny=100))

and histogram the noise

>>> from fipy.variables.histogramVariable import HistogramVariable
>>> histogram = HistogramVariable(distribution=noise, dx=0.01, nx=120, offset=-.1)

>>> if __name__ == '__main__':
...     from fipy import Viewer
...     viewer = Viewer(vars=noise,
...                     datamin=0, datamax=1)
...     histoplot = Viewer(vars=histogram)

>>> from builtins import range
>>> for i in range(10):
...     noise.scramble()
...     if __name__ == '__main__':
...         viewer.plot()
...         histoplot.plot()
random values with a uniform distribution histogram of random values with a uniform distribution
Parameters:

  • mesh (Mesh) – The mesh on which to define the noise.

  • minimum (float) – The minimum (not-inclusive) value of the distribution.

  • maximum (float) – The maximum (not-inclusive) value of the distribution.

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__(points=None, order=0, nearestCellIDs=None)

Interpolates the CellVariable to a set of points using a method that has a memory requirement on the order of Ncells by Npoints in general, but uses only Ncells when the CellVariable’s mesh is a UniformGrid object.

Tests

>>> from fipy import *
>>> m = Grid2D(nx=3, ny=2)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.))))
[ 0.5  1.5  1.5]
>>> print(v(((0., 1.1, 1.2), (0., 1., 1.)), order=1))
[ 0.25  1.1   1.2 ]
>>> m0 = Grid2D(nx=2, ny=2, dx=1., dy=1.)
>>> m1 = Grid2D(nx=4, ny=4, dx=.5, dy=.5)
>>> x, y = m0.cellCenters
>>> v0 = CellVariable(mesh=m0, value=x * y)
>>> print(v0(m1.cellCenters.globalValue))
[ 0.25  0.25  0.75  0.75  0.25  0.25  0.75  0.75  0.75  0.75  2.25  2.25
  0.75  0.75  2.25  2.25]
>>> print(v0(m1.cellCenters.globalValue, order=1))
[ 0.125  0.25   0.5    0.625  0.25   0.375  0.875  1.     0.5    0.875
  1.875  2.25   0.625  1.     2.25   2.625]
Parameters:

  • points (tuple or list of tuple) – A point or set of points in the format (X, Y, Z)

  • order ({0, 1}) – The order of interpolation, default is 0

  • nearestCellIDs (array_like) – Optional argument if user can calculate own nearest cell IDs array, shape should be same as points

__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the CellVariable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new CellVariable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
property arithmeticFaceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
cacheMe(recursive=False)
property cellVolumeAverage

Return the cell-volume-weighted average of the CellVariable:

<\phi>_\text{vol} = \frac{\sum_\text{cells} \phi_\text{cell} V_\text{cell}} {\sum_\text{cells} V_\text{cell}}

>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx = 3, ny = 1, dx = .5, dy = .1)
>>> var = CellVariable(value = (1, 2, 6), mesh = mesh)
>>> print(var.cellVolumeAverage)
3.0
constrain(value, where=None)

Constrains the CellVariable to value at a location specified by where.

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> v.constrain(0., where=m.facesLeft)
>>> v.faceGrad.constrain([1.], where=m.facesRight)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]

Changing the constraint changes the dependencies

>>> v.constrain(1., where=m.facesLeft)
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can be Variable

>>> c = Variable(0.)
>>> v.constrain(c, where=m.facesLeft)
>>> print(v.faceGrad)
[[ 1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> c.value = 1.
>>> print(v.faceGrad)
[[-1.  1.  1.  1.]]
>>> print(v.faceValue)
[ 1.   1.   2.   2.5]

Constraints can have a Variable mask.

>>> v = CellVariable(mesh=m)
>>> mask = FaceVariable(mesh=m, value=m.facesLeft)
>>> v.constrain(1., where=mask)
>>> print(v.faceValue)
[ 1.  0.  0.  0.]
>>> mask[:] = mask | m.facesRight
>>> print(v.faceValue)
[ 1.  0.  0.  1.]
property constraintMask

Test that constraintMask returns a Variable that updates itself whenever the constraints change.

>>> from fipy import *

>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v0 = CellVariable(mesh=m)
>>> v0.constrain(1., where=m.facesLeft)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False False  True False False]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  1.  0.  0.]
>>> v0.constrain(3., where=m.facesRight)
>>> print(v0.faceValue.constraintMask)
[False False False False False False  True False  True  True False  True]
>>> print(v0.faceValue)
[ 0.  0.  0.  0.  0.  0.  1.  0.  3.  1.  0.  3.]
>>> v1 = CellVariable(mesh=m)
>>> v1.constrain(1., where=(x < 1) & (y < 1))
>>> print(v1.constraintMask)
[ True False False False]
>>> print(v1)
[ 1.  0.  0.  0.]
>>> v1.constrain(3., where=(x > 1) & (y > 1))
>>> print(v1.constraintMask)
[ True False False  True]
>>> print(v1)
[ 1.  0.  0.  3.]
property constraints
copy()

Copy the value of the NoiseVariable to a static CellVariable.

dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{self} \cdot ext{other}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

property faceGrad

Return \nabla \phi as a rank-1 FaceVariable using differencing for the normal direction(second-order gradient).

property faceGradAverage

Deprecated since version 3.3: use grad.arithmeticFaceValue() instead

Return \nabla \phi as a rank-1 FaceVariable using averaging for the normal direction(second-order gradient)

property faceValue

Returns a FaceVariable whose value corresponds to the arithmetic interpolation of the adjacent cells:

\phi_f = (\phi_1 - \phi_2) \frac{d_{f2}}{d_{12}} + \phi_2

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (0.5 / 1.) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (1.0 / 3.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.arithmeticFaceValue[mesh.interiorFaces.value]
>>> answer = (R - L) * (5.0 / 55.0) + L
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
property gaussGrad

Return \frac{1}{V_P} \sum_f \vec{n} \phi_f A_f as a rank-1 CellVariable (first-order gradient).

getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
property globalValue

Concatenate and return values from all processors

When running on a single processor, the result is identical to value.

property grad

Return \nabla \phi as a rank-1 CellVariable (first-order gradient).

property harmonicFaceValue

Returns a FaceVariable whose value corresponds to the harmonic interpolation of the adjacent cells:

\phi_f = \frac{\phi_1 \phi_2}{(\phi_2 - \phi_1) \frac{d_{f2}}{d_{12}} + \phi_1}

>>> from fipy.meshes import Grid1D
>>> from fipy import numerix
>>> mesh = Grid1D(dx = (1., 1.))
>>> L = 1
>>> R = 2
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (0.5 / 1.) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (2., 4.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (1.0 / 3.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True

>>> mesh = Grid1D(dx = (10., 100.))
>>> var = CellVariable(mesh = mesh, value = (L, R))
>>> faceValue = var.harmonicFaceValue[mesh.interiorFaces.value]
>>> answer = L * R / ((R - L) * (5.0 / 55.0) + L)
>>> print(numerix.allclose(faceValue, answer, atol = 1e-10, rtol = 1e-10))
True
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property leastSquaresGrad

Return \nabla \phi, which is determined by solving for \nabla \phi in the following matrix equation,

\nabla \phi \cdot \sum_f d_{AP}^2 \vec{n}_{AP} \otimes \vec{n}_{AP} = \sum_f d_{AP}^2 \left( \vec{n} \cdot \nabla \phi \right)_{AP}

The matrix equation is derived by minimizing the following least squares sum,

F \left( \phi_x, \phi_y \right) = \sqrt{\sum_f \left( d_{AP} \vec{n}_{AP} \cdot \nabla \phi - d_{AP} \left( \vec{n}_{AP} \cdot \nabla \phi \right)_{AP} \right)^2 }

Tests

>>> from fipy import Grid2D
>>> m = Grid2D(nx=2, ny=2, dx=0.1, dy=2.0)
>>> print(numerix.allclose(CellVariable(mesh=m, value=(0, 1, 3, 6)).leastSquaresGrad.globalValue, \
...                                     [[8.0, 8.0, 24.0, 24.0],
...                                      [1.2, 2.0, 1.2, 2.0]]))
True

>>> from fipy import Grid1D
>>> print(numerix.allclose(CellVariable(mesh=Grid1D(dx=(2.0, 1.0, 0.5)),
...                                     value=(0, 1, 2)).leastSquaresGrad.globalValue, [[0.461538461538, 0.8, 1.2]]))
True
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
>>> from fipy import Grid2D, CellVariable
>>> mesh = Grid2D(nx=5, ny=5)
>>> x, y = mesh.cellCenters
>>> v = CellVariable(mesh=mesh, value=x*y)
>>> print(v.min())
0.25
property minmodFaceValue

Returns a FaceVariable with a value that is the minimum of the absolute values of the adjacent cells. If the values are of opposite sign then the result is zero:

\phi_f = \begin{cases} \phi_1& \text{when $|\phi_1| \le |\phi_2|$},\\ \phi_2& \text{when $|\phi_2| < |\phi_1|$},\\ 0 & \text{when $\phi1 \phi2 < 0$} \end{cases}

>>> from fipy import *
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(1, 2)).minmodFaceValue)
[1 1 2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, -2)).minmodFaceValue)
[-1 -1 -2]
>>> print(CellVariable(mesh=Grid1D(nx=2), value=(-1, 2)).minmodFaceValue)
[-1  0  2]
property name
property numericValue
property old

Return the values of the CellVariable from the previous solution sweep.

Combinations of CellVariable’s should also return old values.

>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx = 2)
>>> from fipy.variables.cellVariable import CellVariable
>>> var1 = CellVariable(mesh = mesh, value = (2, 3), hasOld = 1)
>>> var2 = CellVariable(mesh = mesh, value = (3, 4))
>>> v = var1 * var2
>>> print(v)
[ 6 12]
>>> var1.value = ((3, 2))
>>> print(v)
[9 8]
>>> print(v.old)
[ 6 12]

The following small test is to correct for a bug when the operator does not just use variables.

>>> v1 = var1 * 3
>>> print(v1)
[9 6]
>>> print(v1.old)
[6 9]
parallelRandom()
put(indices, value)
random()
property rank
ravel()
rdot(other, opShape=None, operatorClass=None)

Return the mesh-element–by–mesh-element (cell-by-cell, face-by-face, etc.) scalar product

ext{other} \cdot ext{self}

Both self and other can be of arbitrary rank, and other does not need to be a _MeshVariable.

release(constraint)

Remove constraint from self

>>> from fipy import *
>>> m = Grid1D(nx=3)
>>> v = CellVariable(mesh=m, value=m.cellCenters[0])
>>> c = Constraint(0., where=m.facesLeft)
>>> v.constrain(c)
>>> print(v.faceValue)
[ 0.   1.   2.   2.5]
>>> v.release(constraint=c)
>>> print(v.faceValue)
[ 0.5  1.   2.   2.5]
scramble()

Generate a new random distribution.

setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape
>>> from fipy.meshes import Grid2D
>>> from fipy.variables.cellVariable import CellVariable
>>> mesh = Grid2D(nx=2, ny=3)
>>> var = CellVariable(mesh=mesh)
>>> print(numerix.allequal(var.shape, (6,))) 
True
>>> print(numerix.allequal(var.arithmeticFaceValue.shape, (17,))) 
True
>>> print(numerix.allequal(var.grad.shape, (2, 6))) 
True
>>> print(numerix.allequal(var.faceGrad.shape, (2, 17))) 
True

Have to account for zero length arrays

>>> from fipy import Grid1D
>>> m = Grid1D(nx=0)
>>> v = CellVariable(mesh=m, elementshape=(2,))
>>> numerix.allequal((v * 1).shape, (2, 0))
True
std(axis=None, **kwargs)

Evaluate standard deviation of all the elements of a MeshVariable.

Adapted from http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/

>>> import fipy as fp
>>> mesh = fp.Grid2D(nx=2, ny=2, dx=2., dy=5.)
>>> var = fp.CellVariable(value=(1., 2., 3., 4.), mesh=mesh)
>>> print((var.std()**2).allclose(1.25))
True
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
updateOld()

Set the values of the previous solution sweep to the current values.

>>> from fipy import *
>>> v = CellVariable(mesh=Grid1D(), hasOld=False)
>>> v.updateOld()
Traceback (most recent call last):
   ...
AssertionError: The updateOld method requires the CellVariable to have an old value. Set hasOld to True when instantiating the CellVariable.
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
class fipy.UpwindConvectionTerm(coeff=1.0, var=None)

Bases: _AbstractUpwindConvectionTerm

The discretization for this Term is given by

\int_V \nabla \cdot (\vec{u} \phi)\,dV \simeq \sum_{f} (\vec{n} \cdot \vec{u})_f \phi_f A_f

where \phi_f=\alpha_f \phi_P +(1-\alpha_f)\phi_A and \alpha_f is calculated using the upwind convection scheme. For further details see Numerical Schemes.

Create a _AbstractConvectionTerm object.

>>> from fipy import *
>>> m = Grid1D(nx = 2)
>>> cv = CellVariable(mesh = m)
>>> fv = FaceVariable(mesh = m)
>>> vcv = CellVariable(mesh=m, rank=1)
>>> vfv = FaceVariable(mesh=m, rank=1)
>>> __ConvectionTerm(coeff = cv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = fv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = vcv)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = vfv)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = (1,))
__ConvectionTerm(coeff=(1,))
>>> ExplicitUpwindConvectionTerm(coeff = (0,)).solve(var=cv, solver=DummySolver()) 
Traceback (most recent call last):
    ...
TransientTermError: The equation requires a TransientTerm with explicit convection.
>>> (TransientTerm(0.) - ExplicitUpwindConvectionTerm(coeff = (0,))).solve(var=cv, solver=DummySolver(), dt=1.)

>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = 1)).solve(var=cv, solver=DummySolver(), dt=1.) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> m2 = Grid2D(nx=2, ny=1)
>>> cv2 = CellVariable(mesh=m2)
>>> vcv2 = CellVariable(mesh=m2, rank=1)
>>> vfv2 = FaceVariable(mesh=m2, rank=1)
>>> __ConvectionTerm(coeff=vcv2)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> __ConvectionTerm(coeff=vfv2)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = ((0,), (0,)))).solve(var=cv2, solver=DummySolver(), dt=1.)
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = (0, 0))).solve(var=cv2, solver=DummySolver(), dt=1.)
Parameters:

coeff (The Term’s coefficient value.) –

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

class fipy.VTKCellViewer(vars, title=None, limits={}, **kwlimits)

Bases: VTKViewer

Renders CellVariable data in VTK format

Creates a VTKViewer

Parameters:

  • vars (CellVariable or FaceVariable or list) – the MeshVariable objects to display.

  • title (str, optional) – displayed at the top of the Viewer window

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

property limits
plot(filename=None)

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

class fipy.VTKFaceViewer(vars, title=None, limits={}, **kwlimits)

Bases: VTKViewer

Renders _MeshVariable data in VTK format

Creates a VTKViewer

Parameters:

  • vars (CellVariable or FaceVariable or list) – the MeshVariable objects to display.

  • title (str, optional) – displayed at the top of the Viewer window

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

property limits
plot(filename=None)

Update the display of the viewed variables.

Parameters:

filename (str) – If not None, the name of a file to save the image into.

plotMesh(filename=None)

Display a representation of the mesh

Parameters:

filename (str) – If not None, the name of a file to save the image into.

setLimits(limits={}, **kwlimits)

Update the limits.

Parameters:

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

property title

The text appearing at the top center.

(default: if len(self.vars) == 1, the name of self.vars[0], otherwise "".)

property vars

The Variable or list of Variable objects to display.

fipy.VTKViewer(vars, title=None, limits={}, **kwlimits)

Generic function for creating a VTKViewer.

The VTKViewer factory will search the module tree and return an instance of the first VTKViewer it finds of the correct dimension and rank.

Parameters:

  • vars (CellVariable or FaceVariable or list) – the MeshVariable objects to display.

  • title (str, optional) – displayed at the top of the Viewer window

  • limits (dict, optional) – a (deprecated) alternative to limit keyword arguments

  • xmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. Any limit set to a (default) value of None will autoscale.

class fipy.VanLeerConvectionTerm(coeff=1.0, var=None)

Bases: ExplicitUpwindConvectionTerm

Create a _AbstractConvectionTerm object.

>>> from fipy import *
>>> m = Grid1D(nx = 2)
>>> cv = CellVariable(mesh = m)
>>> fv = FaceVariable(mesh = m)
>>> vcv = CellVariable(mesh=m, rank=1)
>>> vfv = FaceVariable(mesh=m, rank=1)
>>> __ConvectionTerm(coeff = cv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = fv) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> __ConvectionTerm(coeff = vcv)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = vfv)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.]]), mesh=UniformGrid1D(dx=1.0, nx=2)))
>>> __ConvectionTerm(coeff = (1,))
__ConvectionTerm(coeff=(1,))
>>> ExplicitUpwindConvectionTerm(coeff = (0,)).solve(var=cv, solver=DummySolver()) 
Traceback (most recent call last):
    ...
TransientTermError: The equation requires a TransientTerm with explicit convection.
>>> (TransientTerm(0.) - ExplicitUpwindConvectionTerm(coeff = (0,))).solve(var=cv, solver=DummySolver(), dt=1.)

>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = 1)).solve(var=cv, solver=DummySolver(), dt=1.) 
Traceback (most recent call last):
    ...
VectorCoeffError: The coefficient must be a vector value.
>>> m2 = Grid2D(nx=2, ny=1)
>>> cv2 = CellVariable(mesh=m2)
>>> vcv2 = CellVariable(mesh=m2, rank=1)
>>> vfv2 = FaceVariable(mesh=m2, rank=1)
>>> __ConvectionTerm(coeff=vcv2)
__ConvectionTerm(coeff=_ArithmeticCellToFaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> __ConvectionTerm(coeff=vfv2)
__ConvectionTerm(coeff=FaceVariable(value=array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]]), mesh=UniformGrid2D(dx=1.0, nx=2, dy=1.0, ny=1)))
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = ((0,), (0,)))).solve(var=cv2, solver=DummySolver(), dt=1.)
>>> (TransientTerm() - ExplicitUpwindConvectionTerm(coeff = (0, 0))).solve(var=cv2, solver=DummySolver(), dt=1.)
Parameters:

coeff (The Term’s coefficient value.) –

property RHSvector

Return the RHS vector calculated in solve() or sweep(). The cacheRHSvector() method should be called before solve() or sweep() to cache the vector.

__add__(other)
__and__(other)
__div__(other)
__eq__(other)

Return self==value.

__hash__()

Return hash(self).

__mul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__neg__()

Negate a Term.

>>> -__NonDiffusionTerm(coeff=1.)
__NonDiffusionTerm(coeff=-1.0)
__pos__()
__radd__(other)
__rand__(other)
__repr__()

The representation of a Term object is given by,

>>> print(__UnaryTerm(123.456))
__UnaryTerm(coeff=123.456)
__rmul__(other)

Multiply a term

>>> 2. * __NonDiffusionTerm(coeff=0.5)
__NonDiffusionTerm(coeff=1.0)

Test for ticket:291.

>>> from fipy import PowerLawConvectionTerm
>>> PowerLawConvectionTerm(coeff=[[1], [0]]) * 1.0
PowerLawConvectionTerm(coeff=array([[ 1.],
       [ 0.]]))
__rsub__(other)
__sub__(other)
__truediv__(other)
cacheMatrix()

Informs solve() and sweep() to cache their matrix so that matrix can return the matrix.

cacheRHSvector()

Informs solve() and sweep() to cache their right hand side vector so that getRHSvector() can return it.

copy()
getDefaultSolver(var=None, solver=None, *args, **kwargs)
justErrorVector(var=None, solver=None, boundaryConditions=(), dt=1.0, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the error as well as applying under-relaxation.

justErrorVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy.solvers import DummySolver
>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(DiffusionTerm().justErrorVector(v, solver=DummySolver())) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

error – The residual vector \vec{e}=\mathsf{L}\vec{x}_\text{old} - \vec{b}

Return type:

CellVariable

justResidualVector(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

justResidualVector returns the overlapping local value in parallel (not the non-overlapping value).

>>> from fipy import *
>>> m = Grid1D(nx=10)
>>> v = CellVariable(mesh=m)
>>> len(numerix.asarray(DiffusionTerm().justResidualVector(v))) == m.numberOfCells
True
Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

property matrix

Return the matrix calculated in solve() or sweep(). The cacheMatrix() method should be called before solve() or sweep() to cache the matrix.

residualVectorAndNorm(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None)

Builds the Term’s linear system once.

This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

Returns:

  • residual (~fipy.variables.cellVariable.CellVariable) – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

  • norm (float) – The L2 norm of residual, \|\vec{r}\|_2

solve(var=None, solver=None, boundaryConditions=(), dt=None)

Builds and solves the Term’s linear system once. This method does not return the residual. It should be used when the residual is not required.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

sweep(var=None, solver=None, boundaryConditions=(), dt=None, underRelaxation=None, residualFn=None, cacheResidual=False, cacheError=False)

Builds and solves the Term’s linear system once. This method also recalculates and returns the residual as well as applying under-relaxation.

Parameters:

  • var (CellVariable) – Variable to be solved for. Provides the initial condition, the old value and holds the solution on completion.

  • solver (Solver) – Iterative solver to be used to solve the linear system of equations. The default sovler depends on the solver package selected.

  • boundaryConditions (tuple of BoundaryCondition) –

  • dt (float) – Timestep size.

  • underRelaxation (float) – Usually a value between 0 and 1 or None in the case of no under-relaxation

  • residualFn (function) – Takes var, matrix, and RHSvector arguments, used to customize the residual calculation.

  • cacheResidual (bool) – If True, calculate and store the residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b} in the residualVector member of Term

  • cacheError (bool) – If True, use the residual vector \vec{r} to solve \mathsf{L}\vec{e}=\vec{r} for the error vector \vec{e} and store it in the errorVector member of Term

Returns:

residual – The residual vector \vec{r}=\mathsf{L}\vec{x} - \vec{b}

Return type:

CellVariable

class fipy.Variable(*args, **kwds)

Bases: object

Lazily evaluated quantity with units.

Using a Variable in a mathematical expression will create an automatic dependency Variable, e.g.,

>>> a = Variable(value=3)
>>> b = 4 * a
>>> b
(Variable(value=array(3)) * 4)
>>> b()
12

Changes to the value of a Variable will automatically trigger changes in any dependent Variable objects

>>> a.setValue(5)
>>> b
(Variable(value=array(5)) * 4)
>>> print(b())
20

Create a Variable.

>>> Variable(value=3)
Variable(value=array(3))
>>> Variable(value=3, unit="m")
Variable(value=PhysicalField(3,'m'))
>>> Variable(value=3, unit="m", array=numerix.zeros((3, 2), 'l'))
Variable(value=PhysicalField(array([[3, 3],
       [3, 3],
       [3, 3]]),'m'))
Parameters:

  • value (int or float or array_like) –

  • unit (str or PhysicalUnit) – The physical units of the variable

  • array (ndarray, optional) – The storage array for the Variable

  • name (str) – The user-readable name of the Variable

  • cached (bool) – whether to cache or always recalculate the value

__abs__()

Following test it to fix a bug with C inline string using abs() instead of fabs()

>>> print(abs(Variable(2.3) - Variable(1.2)))
1.1

Check representation works with different versions of numpy

>>> print(repr(abs(Variable(2.3))))
numerix.fabs(Variable(value=array(2.3)))
__add__(other)
__and__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) & (b == 1), [False,  True, False, False]).all())
True
>>> print(a & b)
[0 0 0 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) & (b == 1), [False,  True, False, False]))
True
>>> print(a & b)
[0 0 0 1]
__array__(t=None)

Attempt to convert the Variable to a numerix array object

>>> v = Variable(value=[2, 3])
>>> print(numerix.array(v))
[2 3]

A dimensional Variable will convert to the numeric value in its base units

>>> v = Variable(value=[2, 3], unit="mm")
>>> numerix.array(v)
array([ 0.002,  0.003])
__array_priority__ = 100.0
__array_wrap__(arr, context=None)

Required to prevent numpy not calling the reverse binary operations. Both the following tests are examples ufuncs.

>>> print(type(numerix.array([1.0, 2.0]) * Variable([1.0, 2.0]))) 
<class 'fipy.variables.binaryOperatorVariable...binOp'>

>>> from scipy.special import gamma as Gamma 
>>> print(type(Gamma(Variable([1.0, 2.0])))) 
<class 'fipy.variables.unaryOperatorVariable...unOp'>
__bool__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__call__()

“Evaluate” the Variable and return its value

>>> a = Variable(value=3)
>>> print(a())
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b()
7
__div__(other)
__eq__(other)

Test if a Variable is equal to another quantity

>>> a = Variable(value=3)
>>> b = (a == 4)
>>> b
(Variable(value=array(3)) == 4)
>>> b()
0
__float__()
__ge__(other)

Test if a Variable is greater than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a >= 4)
>>> b
(Variable(value=array(3)) >= 4)
>>> b()
0
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
1
__getitem__(index)

“Evaluate” the Variable and return the specified element

>>> a = Variable(value=((3., 4.), (5., 6.)), unit="m") + "4 m"
>>> print(a[1, 1])
10.0 m

It is an error to slice a Variable whose value is not sliceable

>>> Variable(value=3)[2]
Traceback (most recent call last):
      ...
IndexError: 0-d arrays can't be indexed
__getstate__()

Used internally to collect the necessary information to pickle the Variable to persistent storage.

__gt__(other)

Test if a Variable is greater than another quantity

>>> a = Variable(value=3)
>>> b = (a > 4)
>>> b
(Variable(value=array(3)) > 4)
>>> print(b())
0
>>> a.value = 5
>>> print(b())
1
__hash__()

Return hash(self).

__int__()
__invert__()

Returns logical “not” of the Variable

>>> a = Variable(value=True)
>>> print(~a)
False
__iter__()
__le__(other)

Test if a Variable is less than or equal to another quantity

>>> a = Variable(value=3)
>>> b = (a <= 4)
>>> b
(Variable(value=array(3)) <= 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
1
>>> a.value = 5
>>> print(b())
0
__len__()
__lt__(other)

Test if a Variable is less than another quantity

>>> a = Variable(value=3)
>>> b = (a < 4)
>>> b
(Variable(value=array(3)) < 4)
>>> b()
1
>>> a.value = 4
>>> print(b())
0
>>> print(1000000000000000000 * Variable(1) < 1.)
0
>>> print(1000 * Variable(1) < 1.)
0

Python automatically reverses the arguments when necessary

>>> 4 > Variable(value=3)
(Variable(value=array(3)) < 4)
__mod__(other)
__mul__(other)
__ne__(other)

Test if a Variable is not equal to another quantity

>>> a = Variable(value=3)
>>> b = (a != 4)
>>> b
(Variable(value=array(3)) != 4)
>>> b()
1
__neg__()
static __new__(cls, *args, **kwds)
__nonzero__()
>>> print(bool(Variable(value=0)))
0
>>> print(bool(Variable(value=(0, 0, 1, 1))))
Traceback (most recent call last):
    ...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
__or__(other)

This test case has been added due to a weird bug that was appearing.

>>> a = Variable(value=(0, 0, 1, 1))
>>> b = Variable(value=(0, 1, 0, 1))
>>> print(numerix.equal((a == 0) | (b == 1), [True,  True, False, True]).all())
True
>>> print(a | b)
[0 1 1 1]
>>> from fipy.meshes import Grid1D
>>> mesh = Grid1D(nx=4)
>>> from fipy.variables.cellVariable import CellVariable
>>> a = CellVariable(value=(0, 0, 1, 1), mesh=mesh)
>>> b = CellVariable(value=(0, 1, 0, 1), mesh=mesh)
>>> print(numerix.allequal((a == 0) | (b == 1), [True,  True, False, True]))
True
>>> print(a | b)
[0 1 1 1]
__pos__()
__pow__(other)

return self**other, or self raised to power other

>>> print(Variable(1, "mol/l")**3)
1.0 mol**3/l**3
>>> print((Variable(1, "mol/l")**3).unit)
<PhysicalUnit mol**3/l**3>
__radd__(other)
__rdiv__(other)
__repr__()

Return repr(self).

__rmul__(other)
__rpow__(other)
__rsub__(other)
__rtruediv__(other)
__setitem__(index, value)
__setstate__(dict)

Used internally to create a new Variable from pickled persistent storage.

__str__()

Return str(self).

__sub__(other)
__truediv__(other)
all(axis=None)
>>> print(Variable(value=(0, 0, 1, 1)).all())
0
>>> print(Variable(value=(1, 1, 1, 1)).all())
1
allclose(other, rtol=1e-05, atol=1e-08)
>>> var = Variable((1, 1))
>>> print(var.allclose((1, 1)))
1
>>> print(var.allclose((1,)))
1

The following test is to check that the system does not run out of memory.

>>> from fipy.tools import numerix
>>> var = Variable(numerix.ones(10000))
>>> print(var.allclose(numerix.zeros(10000, 'l')))
False
allequal(other)
any(axis=None)
>>> print(Variable(value=0).any())
0
>>> print(Variable(value=(0, 0, 1, 1)).any())
1
cacheMe(recursive=False)
constrain(value, where=None)

Constrain the Variable to have a value at an index or mask location specified by where.

>>> v = Variable((0, 1, 2, 3))
>>> v.constrain(2, numerix.array((True, False, False, False)))
>>> print(v)
[2 1 2 3]
>>> v[:] = 10
>>> print(v)
[ 2 10 10 10]
>>> v.constrain(5, numerix.array((False, False, True, False)))
>>> print(v)
[ 2 10  5 10]
>>> v[:] = 6
>>> print(v)
[2 6 5 6]
>>> v.constrain(8)
>>> print(v)
[8 8 8 8]
>>> v[:] = 10
>>> print(v)
[8 8 8 8]
>>> del v.constraints[2]
>>> print(v)
[ 2 10  5 10]

>>> from fipy.variables.cellVariable import CellVariable
>>> from fipy.meshes import Grid2D
>>> m = Grid2D(nx=2, ny=2)
>>> x, y = m.cellCenters
>>> v = CellVariable(mesh=m, rank=1, value=(x, y))
>>> v.constrain(((0.,), (-1.,)), where=m.facesLeft)
>>> print(v.faceValue)
[[ 0.5  1.5  0.5  1.5  0.5  1.5  0.   1.   1.5  0.   1.   1.5]
 [ 0.5  0.5  1.   1.   1.5  1.5 -1.   0.5  0.5 -1.   1.5  1.5]]
Parameters:

  • value (float or array_like) – The value of the constraint

  • where (array_like of bool) – The constraint mask or index specifying the location of the constraint

property constraints
copy()

Make an duplicate of the Variable

>>> a = Variable(value=3)
>>> b = a.copy()
>>> b
Variable(value=array(3))

The duplicate will not reflect changes made to the original

>>> a.setValue(5)
>>> b
Variable(value=array(3))

Check that this works for arrays.

>>> a = Variable(value=numerix.array((0, 1, 2)))
>>> b = a.copy()
>>> b
Variable(value=array([0, 1, 2]))
>>> a[1] = 3
>>> b
Variable(value=array([0, 1, 2]))
dontCacheMe(recursive=False)
dot(other, opShape=None, operatorClass=None, axis=0)
getsctype(default=None)

Returns the Numpy sctype of the underlying array.

>>> Variable(1).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1))
True
>>> Variable(1.).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array(1.))
True
>>> Variable((1, 1.)).getsctype() == numerix.NUMERIX.obj2sctype(numerix.array((1., 1.)))
True
inBaseUnits()

Return the value of the Variable with all units reduced to their base SI elements.

>>> e = Variable(value="2.7 Hartree*Nav")
>>> print(e.inBaseUnits().allclose("7088849.01085 kg*m**2/s**2/mol"))
1
inUnitsOf(*units)

Returns one or more Variable objects that express the same physical quantity in different units. The units are specified by strings containing their names. The units must be compatible with the unit of the object. If one unit is specified, the return value is a single Variable.

>>> freeze = Variable('0 degC')
>>> print(freeze.inUnitsOf('degF').allclose("32.0 degF"))
1

If several units are specified, the return value is a tuple of Variable instances with with one element per unit such that the sum of all quantities in the tuple equals the the original quantity and all the values except for the last one are integers. This is used to convert to irregular unit systems like hour/minute/second. The original object will not be changed.

>>> t = Variable(value=314159., unit='s')
>>> from builtins import zip
>>> print(numerix.allclose([e.allclose(v) for (e, v) in zip(t.inUnitsOf('d', 'h', 'min', 's'),
...                                                         ['3.0 d', '15.0 h', '15.0 min', '59.0 s'])],
...                        True))
1
itemset(value)
property itemsize
property mag

The magnitude of the Variable, e.g., |\vec{\psi}| = \sqrt{\vec{\psi}\cdot\vec{\psi}}.

max(axis=None)
min(axis=None)
property name
property numericValue
put(indices, value)
ravel()
release(constraint)

Remove constraint from self

>>> v = Variable((0, 1, 2, 3))
>>> v.constrain(2, numerix.array((True, False, False, False)))
>>> v[:] = 10
>>> from fipy.boundaryConditions.constraint import Constraint
>>> c1 = Constraint(5, numerix.array((False, False, True, False)))
>>> v.constrain(c1)
>>> v[:] = 6
>>> v.constrain(8)
>>> v[:] = 10
>>> del v.constraints[2]
>>> v.release(constraint=c1)
>>> print(v)
[ 2 10 10 10]
setValue(value, unit=None, where=None)

Set the value of the Variable. Can take a masked array.

>>> a = Variable((1, 2, 3))
>>> a.setValue(5, where=(1, 0, 1))
>>> print(a)
[5 2 5]

>>> b = Variable((4, 5, 6))
>>> a.setValue(b, where=(1, 0, 1))
>>> print(a)
[4 2 6]
>>> print(b)
[4 5 6]
>>> a.value = 3
>>> print(a)
[3 3 3]

>>> b = numerix.array((3, 4, 5))
>>> a.value = b
>>> a[:] = 1
>>> print(b)
[3 4 5]

>>> a.setValue((4, 5, 6), where=(1, 0)) 
Traceback (most recent call last):
    ....
ValueError: shape mismatch: objects cannot be broadcast to a single shape
property shape

Tuple of array dimensions.

>>> Variable(value=3).shape
()
>>> numerix.allequal(Variable(value=(3,)).shape, (1,))
True
>>> numerix.allequal(Variable(value=(3, 4)).shape, (2,))
True

>>> Variable(value="3 m").shape
()
>>> numerix.allequal(Variable(value=(3,), unit="m").shape, (1,))
True
>>> numerix.allequal(Variable(value=(3, 4), unit="m").shape, (2,))
True
std(axis=None, **kwargs)
property subscribedVariables
sum(axis=None)
take(ids, axis=0)
tostring(max_line_width=75, precision=8, suppress_small=False, separator=' ')
property unit

Return the unit object of self.

>>> Variable(value="1 m").unit
<PhysicalUnit m>
property value

“Evaluate” the Variable and return its value (longhand)

>>> a = Variable(value=3)
>>> print(a.value)
3
>>> b = a + 4
>>> b
(Variable(value=array(3)) + 4)
>>> b.value
7
exception fipy.VectorCoeffError(s='The coefficient must be a vector value.')

Bases: TypeError

__cause__

exception cause

__context__

exception context

__delattr__(name, /)

Implement delattr(self, name).

__getattribute__(name, /)

Return getattr(self, name).

__reduce__()

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__()
__str__()

Return str(self).

__suppress_context__
__traceback__
add_note()

Exception.add_note(note) – add a note to the exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

fipy.Viewer(vars, title=None, limits={}, FIPY_VIEWER=None, **kwlimits)

Generic function for creating a Viewer.

The Viewer factory will search the module tree and return an instance of the first Viewer it finds that supports the dimensions of vars. Setting the FIPY_VIEWER environment variable to either matplotlib, mayavi, tsv, or vtk will specify the viewer.

The kwlimits or limits parameters can be used to constrain the view. For example:

Viewer(vars=some1Dvar, xmin=0.5, xmax=None, datamax=3)

or:

Viewer(vars=some1Dvar,
       limits={'xmin': 0.5, 'xmax': None, 'datamax': 3})

will return a viewer that displays a line plot from an x value of 0.5 up to the largest x value in the dataset. The data values will be truncated at an upper value of 3, but will have no lower limit.

Parameters:

  • vars (CellVariable or list) – the Variable objects to display.

  • title (str, optional) – displayed at the top of the Viewer window

  • limits (dict) – a (deprecated) alternative to limit keyword arguments

  • FIPY_VIEWER – a specific viewer to attempt (possibly multiple times for multiple variables)

  • xmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • xmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • ymax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • zmax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamin (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

  • datamax (float, optional) – displayed range of data. A 1D Viewer will only use xmin and xmax, a 2D viewer will also use ymin and ymax, and so on. All viewers will use datamin and datamax. Any limit set to a (default) value of None will autoscale.

class fipy.Vitals

Bases: Document

Returns XML formatted information about current FiPy environment

ATTRIBUTE_NODE = 2
CDATA_SECTION_NODE = 4
COMMENT_NODE = 8
DOCUMENT_FRAGMENT_NODE = 11
DOCUMENT_NODE = 9
DOCUMENT_TYPE_NODE = 10
ELEMENT_NODE = 1
ENTITY_NODE = 6
ENTITY_REFERENCE_NODE = 5
NOTATION_NODE = 12
PROCESSING_INSTRUCTION_NODE = 7
TEXT_NODE = 3
__slots__ = ('_elem_info', 'doctype', '_id_search_stack', 'childNodes', '_id_cache')
__str__()

Return str(self).

actualEncoding = None
appendChild(child)
appendInfo(name, svnpath=None, **kwargs)

append some additional information, possibly about a project under a separate svn repository

async_ = False
attributes = None
childNodes
dictToXML(d, name)
doctype
property documentElement

Top-level element of this document.

documentURI = None
encoding = None
errorHandler = None
property firstChild

First child node, or None.

implementation = <xml.dom.minidom.DOMImplementation object>
property lastChild

Last child node, or None.

property localName

Namespace-local name of this node.

namespaceURI = None
nextSibling = None
nodeName = '#document'
nodeType = 9
nodeValue = None
ownerDocument = None
parentNode = None
prefix = None
previousSibling = None
save(fname)
standalone = None
strictErrorChecking = False
svn(*args)
svncmd(cmd, *args)
tupleToXML(t, name, keys=None)
version = None
fipy.openMSHFile(name, dimensions=None, coordDimensions=None, communicator=SerialPETScCommWrapper(), overlap=1, mode='r', background=None)

Open a Gmsh MSH file

Parameters:

  • filename (str) – Gmsh output file

  • dimensions (int) – Dimension of mesh

  • coordDimensions (int) – Dimension of shapes

  • overlap (int) – The number of overlapping cells for parallel simulations. Generally 1 is adequate. Higher order equations or discretizations require more. If overlap is greater than one, communication reverts to serial, as Gmsh only provides one layer of ghost cells.

  • mode (str) – Beginning with r for reading and w for writing. The file will be created if it doesn’t exist when opened for writing; it will be truncated when opened for writing. Add a b to the mode for binary files.

  • background (CellVariable) – Specifies the desired characteristic lengths of the mesh cells

fipy.openPOSFile(name, communicator=SerialPETScCommWrapper(), mode='w')

Open a Gmsh POS post-processing file

fipy.sweepMonotonic(fn, *args, **kwargs)

Repeatedly calls fn(*args, **kwargs)() until the residual returned by fn() is no longer decreasing.

Parameters:

  • fn (function) – The function to call

  • *args –

  • **kwargs –

Return type:

float

Last updated on Jun 27, 2023. Created using Sphinx 6.2.1.