r"""
Vector Space Morphisms (aka Linear Transformations)

AUTHOR:

    - Rob Beezer: (2011-06-29)

A vector space morphism is a homomorphism between vector spaces, better known
as a linear transformation.  These are a specialization of Sage's free module
homomorphisms.  (A free module is like a vector space, but with scalars from a
ring that may not be a field.)  So references to free modules in the
documentation or error messages should be understood as simply reflecting a
more general situation.

Creation
--------

The constructor :func:`linear_transformation` is designed to accept a
variety of inputs that can define a linear transformation.  See the
documentation of the function for all the possibilities.  Here we give two.

First a matrix representation.  By default input matrices are understood
to act on vectors placed to left of the matrix.  Optionally, an input
matrix can be described as acting on vectors placed to the right.  ::

    sage: A = matrix(QQ, [[-1, 2, 3], [4, 2, 0]])
    sage: phi = linear_transformation(A)
    sage: phi
    Vector space morphism represented by the matrix:
    [-1  2  3]
    [ 4  2  0]
    Domain: Vector space of dimension 2 over Rational Field
    Codomain: Vector space of dimension 3 over Rational Field
    sage: phi([2, -3])
    (-14, -2, 6)

A symbolic function can be used to specify the "rule" for a
linear transformation, along with explicit descriptions of the
domain and codomain.  ::

    sage: F = Integers(13)
    sage: D = F^3
    sage: C = F^2
    sage: x, y, z = var('x y z')
    sage: f(x, y, z) = [2*x + 3*y + 5*z, x + z]
    sage: rho = linear_transformation(D, C, f)
    sage: f(1, 2, 3)
    (23, 4)
    sage: rho([1, 2, 3])
    (10, 4)

A "vector space homspace" is the set of all linear transformations
between two vector spaces.  Various input can be coerced into a
homspace to create a linear transformation.  See
:mod:`sage.modules.vector_space_homspace` for more. ::

    sage: D = QQ^4
    sage: C = QQ^2
    sage: hom_space = Hom(D, C)
    sage: images = [[1, 3], [2, -1], [4, 0], [3, 7]]
    sage: zeta = hom_space(images)
    sage: zeta
    Vector space morphism represented by the matrix:
    [ 1  3]
    [ 2 -1]
    [ 4  0]
    [ 3  7]
    Domain: Vector space of dimension 4 over Rational Field
    Codomain: Vector space of dimension 2 over Rational Field

A homomorphism may also be created via a method on the domain.  ::

    sage: F = QQ[sqrt(3)]
    sage: a = F.gen(0)
    sage: D = F^2
    sage: C = F^2
    sage: A = matrix(F, [[a, 1], [2*a, 2]])
    sage: psi = D.hom(A, C)
    sage: psi
    Vector space morphism represented by the matrix:
    [  sqrt3       1]
    [2*sqrt3       2]
    Domain: Vector space of dimension 2 over Number Field in sqrt3 with defining polynomial x^2 - 3
    Codomain: Vector space of dimension 2 over Number Field in sqrt3 with defining polynomial x^2 - 3
    sage: psi([1, 4])
    (9*sqrt3, 9)

Properties
----------

Many natural properties of a linear transformation can be computed.
Some of these are more general methods of objects in the classes
:class:`sage.modules.free_module_morphism.FreeModuleMorphism` and
:class:`sage.modules.matrix_morphism.MatrixMorphism`.

Values are computed in a natural way, an inverse image of an
element can be computed with the ``lift()`` method, when the inverse
image actually exists.  ::

    sage: A = matrix(QQ, [[1,2], [2,4], [3,6]])
    sage: phi = linear_transformation(A)
    sage: phi([1,2,0])
    (5, 10)
    sage: phi.lift([10, 20])
    (10, 0, 0)
    sage: phi.lift([100, 100])
    Traceback (most recent call last):
    ...
    ValueError: element is not in the image

Images and pre-images can be computed as vector spaces.  ::

    sage: A = matrix(QQ, [[1,2], [2,4], [3,6]])
    sage: phi = linear_transformation(A)
    sage: phi.image()
    Vector space of degree 2 and dimension 1 over Rational Field
    Basis matrix:
    [1 2]

    sage: phi.inverse_image( (QQ^2).span([[1,2]]) )
    Vector space of degree 3 and dimension 3 over Rational Field
    Basis matrix:
    [1 0 0]
    [0 1 0]
    [0 0 1]

    sage: phi.inverse_image( (QQ^2).span([[1,1]]) )
    Vector space of degree 3 and dimension 2 over Rational Field
    Basis matrix:
    [   1    0 -1/3]
    [   0    1 -2/3]

Injectivity and surjectivity can be checked.  ::

    sage: A = matrix(QQ, [[1,2], [2,4], [3,6]])
    sage: phi = linear_transformation(A)
    sage: phi.is_injective()
    False
    sage: phi.is_surjective()
    False

Restrictions and Representations
--------------------------------

It is possible to restrict the domain and codomain of a linear
transformation to make a new linear transformation.  We will use
those commands to replace the domain and codomain by equal vector
spaces, but with alternate bases.  The point here is that the
matrix representation used to represent linear transformations are
relative to the bases of both the domain and codomain. ::

    sage: A = graphs.PetersenGraph().adjacency_matrix()
    sage: V = QQ^10
    sage: phi = linear_transformation(V, V, A)
    sage: phi
    Vector space morphism represented by the matrix:
    [0 1 0 0 1 1 0 0 0 0]
    [1 0 1 0 0 0 1 0 0 0]
    [0 1 0 1 0 0 0 1 0 0]
    [0 0 1 0 1 0 0 0 1 0]
    [1 0 0 1 0 0 0 0 0 1]
    [1 0 0 0 0 0 0 1 1 0]
    [0 1 0 0 0 0 0 0 1 1]
    [0 0 1 0 0 1 0 0 0 1]
    [0 0 0 1 0 1 1 0 0 0]
    [0 0 0 0 1 0 1 1 0 0]
    Domain: Vector space of dimension 10 over Rational Field
    Codomain: Vector space of dimension 10 over Rational Field

    sage: B1 = [V.gen(i) + V.gen(i+1) for i in range(9)] + [V.gen(9)]
    sage: B2 = [V.gen(0)] + [-V.gen(i-1) + V.gen(i) for i in range(1,10)]
    sage: D = V.subspace_with_basis(B1)
    sage: C = V.subspace_with_basis(B2)
    sage: rho = phi.restrict_codomain(C)
    sage: zeta = rho.restrict_domain(D)
    sage: zeta
    Vector space morphism represented by the matrix:
    [6 5 4 3 3 2 1 0 0 0]
    [6 5 4 3 2 2 2 1 0 0]
    [6 6 5 4 3 2 2 2 1 0]
    [6 5 5 4 3 2 2 2 2 1]
    [6 4 4 4 3 3 3 3 2 1]
    [6 5 4 4 4 4 4 4 3 1]
    [6 6 5 4 4 4 3 3 3 2]
    [6 6 6 5 4 4 2 1 1 1]
    [6 6 6 6 5 4 3 1 0 0]
    [3 3 3 3 3 2 2 1 0 0]
    Domain: Vector space of degree 10 and dimension 10 over Rational Field
    User basis matrix:
    [1 1 0 0 0 0 0 0 0 0]
    [0 1 1 0 0 0 0 0 0 0]
    [0 0 1 1 0 0 0 0 0 0]
    [0 0 0 1 1 0 0 0 0 0]
    [0 0 0 0 1 1 0 0 0 0]
    [0 0 0 0 0 1 1 0 0 0]
    [0 0 0 0 0 0 1 1 0 0]
    [0 0 0 0 0 0 0 1 1 0]
    [0 0 0 0 0 0 0 0 1 1]
    [0 0 0 0 0 0 0 0 0 1]
    Codomain: Vector space of degree 10 and dimension 10 over Rational Field
    User basis matrix:
    [ 1  0  0  0  0  0  0  0  0  0]
    [-1  1  0  0  0  0  0  0  0  0]
    [ 0 -1  1  0  0  0  0  0  0  0]
    [ 0  0 -1  1  0  0  0  0  0  0]
    [ 0  0  0 -1  1  0  0  0  0  0]
    [ 0  0  0  0 -1  1  0  0  0  0]
    [ 0  0  0  0  0 -1  1  0  0  0]
    [ 0  0  0  0  0  0 -1  1  0  0]
    [ 0  0  0  0  0  0  0 -1  1  0]
    [ 0  0  0  0  0  0  0  0 -1  1]

An endomorphism is a linear transformation with an equal domain and codomain,
and here each needs to have the same basis.  We are using a
matrix that has well-behaved eigenvalues, as part of showing that these
do not change as the representation changes.  ::

    sage: A = graphs.PetersenGraph().adjacency_matrix()
    sage: V = QQ^10
    sage: phi = linear_transformation(V, V, A)
    sage: phi.eigenvalues()
    [3, -2, -2, -2, -2, 1, 1, 1, 1, 1]

    sage: B1 = [V.gen(i) + V.gen(i+1) for i in range(9)] + [V.gen(9)]
    sage: C = V.subspace_with_basis(B1)
    sage: zeta = phi.restrict(C)
    sage: zeta
    Vector space morphism represented by the matrix:
    [ 1  0  1 -1  2 -1  2 -2  2 -2]
    [ 1  0  1  0  0  0  1  0  0  0]
    [ 0  1  0  1  0  0  0  1  0  0]
    [ 1 -1  2 -1  2 -2  2 -2  3 -2]
    [ 2 -2  2 -1  1 -1  1  0  1  0]
    [ 1  0  0  0  0  0  0  1  1  0]
    [ 0  1  0  0  0  1 -1  1  0  2]
    [ 0  0  1  0  0  2 -1  1 -1  2]
    [ 0  0  0  1  0  1  1  0  0  0]
    [ 0  0  0  0  1 -1  2 -1  1 -1]
    Domain: Vector space of degree 10 and dimension 10 over Rational Field
    User basis matrix:
    [1 1 0 0 0 0 0 0 0 0]
    [0 1 1 0 0 0 0 0 0 0]
    [0 0 1 1 0 0 0 0 0 0]
    [0 0 0 1 1 0 0 0 0 0]
    [0 0 0 0 1 1 0 0 0 0]
    [0 0 0 0 0 1 1 0 0 0]
    [0 0 0 0 0 0 1 1 0 0]
    [0 0 0 0 0 0 0 1 1 0]
    [0 0 0 0 0 0 0 0 1 1]
    [0 0 0 0 0 0 0 0 0 1]
    Codomain: Vector space of degree 10 and dimension 10 over Rational Field
    User basis matrix:
    [1 1 0 0 0 0 0 0 0 0]
    [0 1 1 0 0 0 0 0 0 0]
    [0 0 1 1 0 0 0 0 0 0]
    [0 0 0 1 1 0 0 0 0 0]
    [0 0 0 0 1 1 0 0 0 0]
    [0 0 0 0 0 1 1 0 0 0]
    [0 0 0 0 0 0 1 1 0 0]
    [0 0 0 0 0 0 0 1 1 0]
    [0 0 0 0 0 0 0 0 1 1]
    [0 0 0 0 0 0 0 0 0 1]

    sage: zeta.eigenvalues()
    [3, -2, -2, -2, -2, 1, 1, 1, 1, 1]

Equality
--------

Equality of linear transformations is a bit nuanced.  The equality operator
``==`` tests if two linear transformations have equal matrix representations,
while we determine if two linear transformations are the same function with the
``.is_equal_function()`` method.  Notice in this example that the function
never changes, just the representations.  ::

    sage: f = lambda x: vector(QQ, [x[1], x[0]+x[1], x[0]])
    sage: H = Hom(QQ^2, QQ^3)
    sage: phi = H(f)

    sage: rho = linear_transformation(QQ^2, QQ^3, matrix(QQ,2, 3, [[0,1,1], [1,1,0]]))

    sage: phi == rho
    True

    sage: U = (QQ^2).subspace_with_basis([[1, 2], [-3, 1]])
    sage: V = (QQ^3).subspace_with_basis([[0, 1, 0], [2, 3, 1], [-1, 1, 6]])
    sage: K = Hom(U, V)
    sage: zeta = K(f)

    sage: zeta == phi
    False
    sage: zeta.is_equal_function(phi)
    True
    sage: zeta.is_equal_function(rho)
    True

TESTS::

    sage: V = QQ^2
    sage: H = Hom(V, V)
    sage: f = H([V.1,-2*V.0])
    sage: loads(dumps(f))
    Vector space morphism represented by the matrix:
    [ 0  1]
    [-2  0]
    Domain: Vector space of dimension 2 over Rational Field
    Codomain: Vector space of dimension 2 over Rational Field
    sage: loads(dumps(f)) == f
    True
"""
from __future__ import absolute_import

####################################################################################
#       Copyright (C) 2011 Rob Beezer <beezer@ups.edu>
#
#  Distributed under the terms of the GNU General Public License (GPL)
#
#    This code is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
#    General Public License for more details.
#
#  The full text of the GPL is available at:
#
#                  http://www.gnu.org/licenses/
####################################################################################


import sage.modules.matrix_morphism as matrix_morphism
import sage.modules.free_module_morphism as free_module_morphism
from . import vector_space_homspace
from sage.matrix.matrix import is_Matrix

def linear_transformation(arg0, arg1=None, arg2=None, side='left'):
    r"""
    Create a linear transformation from a variety of possible inputs.

    FORMATS:

    In the following, ``D`` and ``C`` are vector spaces over
    the same field that are the domain and codomain
    (respectively) of the linear transformation.

    ``side`` is a keyword that is either 'left' or 'right'.
    When a matrix is used to specify a linear transformation,
    as in the first two call formats below, you may specify
    if the function is given by matrix multiplication with
    the vector on the left, or the vector on the right.
    The default is 'left'. Internally representations are
    always carried as the 'left' version, and the default
    text representation is this version.  However, the matrix
    representation may be obtained as either version, no matter
    how it is created.

    - ``linear_transformation(A, side='left')``

      Where ``A`` is a matrix.  The domain and codomain are inferred
      from the dimension of the matrix and the base ring of the matrix.
      The base ring must be a field, or have its fraction field implemented
      in Sage.

    - ``linear_transformation(D, C, A, side='left')``

      ``A`` is a matrix that behaves as above.  However, now the domain
      and codomain are given explicitly. The matrix is checked for
      compatibility with the domain and codomain.  Additionally, the
      domain and codomain may be supplied with alternate ("user") bases
      and the matrix is interpreted as being a representation relative
      to those bases.

    - ``linear_transformation(D, C, f)``

      ``f`` is any function that can be applied to the basis elements of the
      domain and that produces elements of the codomain.  The linear
      transformation returned is the unique linear transformation that
      extends this mapping on the basis elements.  ``f`` may come from a
      function defined by a Python ``def`` statement, or may be defined as a
      ``lambda`` function.

      Alternatively, ``f`` may be specified by a callable symbolic function,
      see the examples below for a demonstration.

    - ``linear_transformation(D, C, images)``

      ``images`` is a list, or tuple, of codomain elements, equal in number
      to the size of the basis of the domain.  Each basis element of the domain
      is mapped to the corresponding element of the ``images`` list, and the
      linear transformation returned is the unique linear transformation that
      extends this mapping.

    OUTPUT:

    A linear transformation described by the input.  This is a
    "vector space morphism", an object of the class
    :class:`sage.modules.vector_space_morphism`.

    EXAMPLES:

    We can define a linear transformation with just a matrix, understood to
    act on a vector placed on one side or the other.  The field for the
    vector spaces used as domain and codomain is obtained from the base
    ring of the matrix, possibly promoting to a fraction field.  ::

        sage: A = matrix(ZZ, [[1, -1, 4], [2, 0, 5]])
        sage: phi = linear_transformation(A)
        sage: phi
        Vector space morphism represented by the matrix:
        [ 1 -1  4]
        [ 2  0  5]
        Domain: Vector space of dimension 2 over Rational Field
        Codomain: Vector space of dimension 3 over Rational Field
        sage: phi([1/2, 5])
        (21/2, -1/2, 27)

        sage: B = matrix(Integers(7), [[1, 2, 1], [3, 5, 6]])
        sage: rho = linear_transformation(B, side='right')
        sage: rho
        Vector space morphism represented by the matrix:
        [1 3]
        [2 5]
        [1 6]
        Domain: Vector space of dimension 3 over Ring of integers modulo 7
        Codomain: Vector space of dimension 2 over Ring of integers modulo 7
        sage: rho([2, 4, 6])
        (2, 6)

    We can define a linear transformation with a matrix, while explicitly
    giving the domain and codomain.  Matrix entries will be coerced into the
    common field of scalars for the vector spaces.  ::

        sage: D = QQ^3
        sage: C = QQ^2
        sage: A = matrix([[1, 7], [2, -1], [0, 5]])
        sage: A.parent()
        Full MatrixSpace of 3 by 2 dense matrices over Integer Ring
        sage: zeta = linear_transformation(D, C, A)
        sage: zeta.matrix().parent()
        Full MatrixSpace of 3 by 2 dense matrices over Rational Field
        sage: zeta
        Vector space morphism represented by the matrix:
        [ 1  7]
        [ 2 -1]
        [ 0  5]
        Domain: Vector space of dimension 3 over Rational Field
        Codomain: Vector space of dimension 2 over Rational Field

    Matrix representations are relative to the bases for the domain
    and codomain.  ::

        sage: u = vector(QQ, [1, -1])
        sage: v = vector(QQ, [2, 3])
        sage: D = (QQ^2).subspace_with_basis([u, v])
        sage: x = vector(QQ, [2, 1])
        sage: y = vector(QQ, [-1, 4])
        sage: C = (QQ^2).subspace_with_basis([x, y])
        sage: A = matrix(QQ, [[2, 5], [3, 7]])
        sage: psi = linear_transformation(D, C, A)
        sage: psi
        Vector space morphism represented by the matrix:
        [2 5]
        [3 7]
        Domain: Vector space of degree 2 and dimension 2 over Rational Field
        User basis matrix:
        [ 1 -1]
        [ 2  3]
        Codomain: Vector space of degree 2 and dimension 2 over Rational Field
        User basis matrix:
        [ 2  1]
        [-1  4]
        sage: psi(u) == 2*x + 5*y
        True
        sage: psi(v) == 3*x + 7*y
        True

    Functions that act on the domain may be used to compute images of
    the domain's basis elements, and this mapping can be extended to
    a unique linear transformation.  The function may be a Python
    function (via ``def`` or ``lambda``) or a Sage symbolic function.  ::

        sage: def g(x):
        ....:     return vector(QQ, [2*x[0]+x[2], 5*x[1]])
        sage: phi = linear_transformation(QQ^3, QQ^2, g)
        sage: phi
        Vector space morphism represented by the matrix:
        [2 0]
        [0 5]
        [1 0]
        Domain: Vector space of dimension 3 over Rational Field
        Codomain: Vector space of dimension 2 over Rational Field

        sage: f = lambda x: vector(QQ, [2*x[0]+x[2], 5*x[1]])
        sage: rho = linear_transformation(QQ^3, QQ^2, f)
        sage: rho
        Vector space morphism represented by the matrix:
        [2 0]
        [0 5]
        [1 0]
        Domain: Vector space of dimension 3 over Rational Field
        Codomain: Vector space of dimension 2 over Rational Field

        sage: x, y, z = var('x y z')
        sage: h(x, y, z) = [2*x + z, 5*y]
        sage: zeta = linear_transformation(QQ^3, QQ^2, h)
        sage: zeta
        Vector space morphism represented by the matrix:
        [2 0]
        [0 5]
        [1 0]
        Domain: Vector space of dimension 3 over Rational Field
        Codomain: Vector space of dimension 2 over Rational Field

        sage: phi == rho
        True
        sage: rho == zeta
        True


    We create a linear transformation relative to non-standard bases,
    and capture its representation relative to standard bases.  With this, we
    can build functions that create the same linear transformation relative
    to the nonstandard bases.  ::

        sage: u = vector(QQ, [1, -1])
        sage: v = vector(QQ, [2, 3])
        sage: D = (QQ^2).subspace_with_basis([u, v])
        sage: x = vector(QQ, [2, 1])
        sage: y = vector(QQ, [-1, 4])
        sage: C = (QQ^2).subspace_with_basis([x, y])
        sage: A = matrix(QQ, [[2, 5], [3, 7]])
        sage: psi = linear_transformation(D, C, A)
        sage: rho = psi.restrict_codomain(QQ^2).restrict_domain(QQ^2)
        sage: rho.matrix()
        [ -4/5  97/5]
        [  1/5 -13/5]

        sage: f = lambda x: vector(QQ, [(-4/5)*x[0] + (1/5)*x[1], (97/5)*x[0] + (-13/5)*x[1]])
        sage: psi = linear_transformation(D, C, f)
        sage: psi.matrix()
        [2 5]
        [3 7]

        sage: s, t = var('s t')
        sage: h(s, t) = [(-4/5)*s + (1/5)*t, (97/5)*s + (-13/5)*t]
        sage: zeta = linear_transformation(D, C, h)
        sage: zeta.matrix()
        [2 5]
        [3 7]

    Finally, we can give an explicit list of images for the basis
    elements of the domain.  ::

        sage: x = polygen(QQ)
        sage: F.<a> = NumberField(x^3+x+1)
        sage: u = vector(F, [1, a, a^2])
        sage: v = vector(F, [a, a^2, 2])
        sage: w = u + v
        sage: D = F^3
        sage: C = F^3
        sage: rho = linear_transformation(D, C, [u, v, w])
        sage: rho.matrix()
        [      1       a     a^2]
        [      a     a^2       2]
        [  a + 1 a^2 + a a^2 + 2]
        sage: C = (F^3).subspace_with_basis([u, v])
        sage: D = (F^3).subspace_with_basis([u, v])
        sage: psi = linear_transformation(C, D, [u+v, u-v])
        sage: psi.matrix()
        [ 1  1]
        [ 1 -1]

    TESTS:

    We test some bad inputs.  First, the wrong things in the wrong places.  ::

        sage: linear_transformation('junk')
        Traceback (most recent call last):
        ...
        TypeError: first argument must be a matrix or a vector space, not junk

        sage: linear_transformation(QQ^2, QQ^3, 'stuff')
        Traceback (most recent call last):
        ...
        TypeError: third argument must be a matrix, function, or list of images, not stuff

        sage: linear_transformation(QQ^2, 'garbage')
        Traceback (most recent call last):
        ...
        TypeError: if first argument is a vector space, then second argument must be a vector space, not garbage

        sage: linear_transformation(QQ^2, Integers(7)^2)
        Traceback (most recent call last):
        ...
        TypeError: vector spaces must have the same field of scalars, not Rational Field and Ring of integers modulo 7

    Matrices must be over a field (or a ring that can be promoted to a field),
    and of the right size.  ::

        sage: linear_transformation(matrix(Integers(6), [[2, 3],[4, 5]]))
        Traceback (most recent call last):
        ...
        TypeError: matrix must have entries from a field, or a ring with a fraction field, not Ring of integers modulo 6

        sage: A = matrix(QQ, 3, 4, range(12))
        sage: linear_transformation(QQ^4, QQ^4, A)
        Traceback (most recent call last):
        ...
        TypeError: domain dimension is incompatible with matrix size

        sage: linear_transformation(QQ^3, QQ^3, A, side='right')
        Traceback (most recent call last):
        ...
        TypeError: domain dimension is incompatible with matrix size

        sage: linear_transformation(QQ^3, QQ^3, A)
        Traceback (most recent call last):
        ...
        TypeError: codomain dimension is incompatible with matrix size

        sage: linear_transformation(QQ^4, QQ^4, A, side='right')
        Traceback (most recent call last):
        ...
        TypeError: codomain dimension is incompatible with matrix size

    Lists of images can be of the wrong number, or not really
    elements of the codomain.  ::

        sage: linear_transformation(QQ^3, QQ^2, [vector(QQ, [1,2])])
        Traceback (most recent call last):
        ...
        ValueError: number of images should equal the size of the domain's basis (=3), not 1

        sage: C = (QQ^2).subspace_with_basis([vector(QQ, [1,1])])
        sage: linear_transformation(QQ^1, C, [vector(QQ, [1,2])])
        Traceback (most recent call last):
        ...
        ArithmeticError: some proposed image is not in the codomain, because
        element [1, 2] is not in free module


    Functions may not apply properly to domain elements,
    or return values outside the codomain.  ::

        sage: f = lambda x: vector(QQ, [x[0], x[4]])
        sage: linear_transformation(QQ^3, QQ^2, f)
        Traceback (most recent call last):
        ...
        ValueError: function cannot be applied properly to some basis element because
        vector index out of range

        sage: f = lambda x: vector(QQ, [x[0], x[1]])
        sage: C = (QQ^2).span([vector(QQ, [1, 1])])
        sage: linear_transformation(QQ^2, C, f)
        Traceback (most recent call last):
        ...
        ArithmeticError: some image of the function is not in the codomain, because
        element [1, 0] is not in free module

    A Sage symbolic function can come in a variety of forms that are
    not representative of a linear transformation. ::

        sage: x, y = var('x, y')
        sage: f(x, y) = [y, x, y]
        sage: linear_transformation(QQ^3, QQ^3, f)
        Traceback (most recent call last):
        ...
        ValueError: symbolic function has the wrong number of inputs for domain

        sage: linear_transformation(QQ^2, QQ^2, f)
        Traceback (most recent call last):
        ...
        ValueError: symbolic function has the wrong number of outputs for codomain

        sage: x, y = var('x y')
        sage: f(x, y) = [y, x*y]
        sage: linear_transformation(QQ^2, QQ^2, f)
        Traceback (most recent call last):
        ...
        ValueError: symbolic function must be linear in all the inputs:
        unable to convert y to a rational

        sage: x, y = var('x y')
        sage: f(x, y) = [x, 2*y]
        sage: C = (QQ^2).span([vector(QQ, [1, 1])])
        sage: linear_transformation(QQ^2, C, f)
        Traceback (most recent call last):
        ...
        ArithmeticError: some image of the function is not in the codomain, because
        element [1, 0] is not in free module
    """
    from sage.matrix.constructor import matrix
    from sage.modules.module import is_VectorSpace
    from sage.modules.free_module import VectorSpace
    from sage.categories.homset import Hom
    from sage.symbolic.ring import SR
    from sage.modules.vector_callable_symbolic_dense import Vector_callable_symbolic_dense
    from inspect import isfunction

    if not side in ['left', 'right']:
        raise ValueError("side must be 'left' or 'right', not {0}".format(side))
    if not (is_Matrix(arg0) or is_VectorSpace(arg0)):
        raise TypeError('first argument must be a matrix or a vector space, not {0}'.format(arg0))
    if is_Matrix(arg0):
        R = arg0.base_ring()
        if not R.is_field():
            try:
                R = R.fraction_field()
            except (NotImplementedError, TypeError):
                msg = 'matrix must have entries from a field, or a ring with a fraction field, not {0}'
                raise TypeError(msg.format(R))
        if side == 'right':
            arg0 = arg0.transpose()
            side = 'left'
        arg2 = arg0
        arg0 = VectorSpace(R, arg2.nrows())
        arg1 = VectorSpace(R, arg2.ncols())
    elif is_VectorSpace(arg0):
        if not is_VectorSpace(arg1):
            msg = 'if first argument is a vector space, then second argument must be a vector space, not {0}'
            raise TypeError(msg.format(arg1))
        if arg0.base_ring() != arg1.base_ring():
            msg = 'vector spaces must have the same field of scalars, not {0} and {1}'
            raise TypeError(msg.format(arg0.base_ring(), arg1.base_ring()))

    # Now arg0 = domain D, arg1 = codomain C, and
    #   both are vector spaces with common field of scalars
    #   use these to make a VectorSpaceHomSpace
    # arg2 might be a matrix that began in arg0
    D = arg0
    C = arg1
    H = Hom(D, C, category=None)

    # Examine arg2 as the "rule" for the linear transformation
    # Pass on matrices, Python functions and lists to homspace call
    # Convert symbolic function here, to a matrix
    if is_Matrix(arg2):
        if side == 'right':
            arg2 = arg2.transpose()
    elif isinstance(arg2, (list, tuple)):
        pass
    elif isfunction(arg2):
        pass
    elif isinstance(arg2, Vector_callable_symbolic_dense):
        args = arg2.parent().base_ring()._arguments
        exprs = arg2.change_ring(SR)
        m = len(args)
        n = len(exprs)
        if m != D.degree():
            raise ValueError('symbolic function has the wrong number of inputs for domain')
        if n != C.degree():
            raise ValueError('symbolic function has the wrong number of outputs for codomain')
        arg2 = [[e.coefficient(a) for e in exprs] for a in args]
        try:
            arg2 = matrix(D.base_ring(), m, n, arg2)
        except TypeError as e:
            msg = 'symbolic function must be linear in all the inputs:\n' + e.args[0]
            raise ValueError(msg)
        # have matrix with respect to standard bases, now consider user bases
        images = [v*arg2 for v in D.basis()]
        try:
            arg2 = matrix([C.coordinates(C(a)) for a in images])
        except (ArithmeticError, TypeError) as e:
            msg = 'some image of the function is not in the codomain, because\n' + e.args[0]
            raise ArithmeticError(msg)
    else:
        msg = 'third argument must be a matrix, function, or list of images, not {0}'
        raise TypeError(msg.format(arg2))

    # arg2 now compatible with homspace H call method
    # __init__ will check matrix sizes versus domain/codomain dimensions
    return H(arg2)

def is_VectorSpaceMorphism(x):
    r"""
    Returns ``True`` if ``x`` is a vector space morphism (a linear transformation).

    INPUT:

    ``x`` - anything

    OUTPUT:

    ``True`` only if ``x`` is an instance of a vector space morphism,
    which are also known as linear transformations.

    EXAMPLES::

        sage: V = QQ^2; f = V.hom([V.1,-2*V.0])
        sage: sage.modules.vector_space_morphism.is_VectorSpaceMorphism(f)
        True
        sage: sage.modules.vector_space_morphism.is_VectorSpaceMorphism('junk')
        False
    """
    return isinstance(x, VectorSpaceMorphism)


class VectorSpaceMorphism(free_module_morphism.FreeModuleMorphism):

    def __init__(self, homspace, A):
        r"""
        Create a linear transformation, a morphism between vector spaces.

        INPUT:

        -  ``homspace`` - a homspace (of vector spaces) to serve
           as a parent for the linear transformation and a home for
           the domain and codomain of the morphism
        -  ``A`` - a matrix representing the linear transformation,
           which will act on vectors placed to the left of the matrix

        EXAMPLES:

        Nominally, we require a homspace to hold the domain
        and codomain and a matrix representation of the morphism
        (linear transformation).  ::

            sage: from sage.modules.vector_space_homspace import VectorSpaceHomspace
            sage: from sage.modules.vector_space_morphism import VectorSpaceMorphism
            sage: H = VectorSpaceHomspace(QQ^3, QQ^2)
            sage: A = matrix(QQ, 3, 2, range(6))
            sage: zeta = VectorSpaceMorphism(H, A)
            sage: zeta
            Vector space morphism represented by the matrix:
            [0 1]
            [2 3]
            [4 5]
            Domain: Vector space of dimension 3 over Rational Field
            Codomain: Vector space of dimension 2 over Rational Field

        See the constructor,
        :func:`sage.modules.vector_space_morphism.linear_transformation`
        for another way to create linear transformations.

        The ``.hom()`` method of a vector space will create a vector
        space morphism. ::

            sage: V = QQ^3; W = V.subspace_with_basis([[1,2,3], [-1,2,5/3], [0,1,-1]])
            sage: phi = V.hom(matrix(QQ, 3, range(9)), codomain=W) # indirect doctest
            sage: type(phi)
            <class 'sage.modules.vector_space_morphism.VectorSpaceMorphism'>

        A matrix may be coerced into a vector space homspace to
        create a vector space morphism.  ::

            sage: from sage.modules.vector_space_homspace import VectorSpaceHomspace
            sage: H = VectorSpaceHomspace(QQ^3, QQ^2)
            sage: A = matrix(QQ, 3, 2, range(6))
            sage: rho = H(A)  # indirect doctest
            sage: type(rho)
            <class 'sage.modules.vector_space_morphism.VectorSpaceMorphism'>
        """
        if not vector_space_homspace.is_VectorSpaceHomspace(homspace):
            raise TypeError('homspace must be a vector space hom space, not {0}'.format(homspace))
        if isinstance(A, matrix_morphism.MatrixMorphism):
            A = A.matrix()
        if not is_Matrix(A):
            msg = 'input must be a matrix representation or another matrix morphism, not {0}'
            raise TypeError(msg.format(A))
        # now have a vector space homspace, and a matrix, check compatibility

        if homspace.domain().dimension() != A.nrows():
            raise TypeError('domain dimension is incompatible with matrix size')
        if homspace.codomain().dimension() != A.ncols():
            raise TypeError('codomain dimension is incompatible with matrix size')

        A = homspace._matrix_space()(A)
        free_module_morphism.FreeModuleMorphism.__init__(self, homspace, A)

    def is_invertible(self):
        r"""
        Determines if the vector space morphism has an inverse.

        OUTPUT:

        ``True`` if the vector space morphism is invertible, otherwise
        ``False``.

        EXAMPLES:

        If the dimension of the domain does not match the dimension
        of the codomain, then the morphism cannot be invertible.  ::

            sage: V = QQ^3
            sage: U = V.subspace_with_basis([V.0 + V.1, 2*V.1 + 3*V.2])
            sage: phi = V.hom([U.0, U.0 + U.1, U.0 - U.1], U)
            sage: phi.is_invertible()
            False

        An invertible linear transformation. ::

            sage: A = matrix(QQ, 3, [[-3, 5, -5], [4, -7, 7], [6, -8, 10]])
            sage: A.determinant()
            2
            sage: H = Hom(QQ^3, QQ^3)
            sage: rho = H(A)
            sage: rho.is_invertible()
            True

        A non-invertible linear transformation, an endomorphism of
        a vector space over a finite field.  ::

            sage: F.<a> = GF(11^2)
            sage: A = matrix(F, [[6*a + 3,   8*a +  2, 10*a + 3],
            ....:                [2*a + 7,   4*a +  3,  2*a + 3],
            ....:                [9*a + 2,  10*a + 10,  3*a + 3]])
            sage: A.nullity()
            1
            sage: E = End(F^3)
            sage: zeta = E(A)
            sage: zeta.is_invertible()
            False
        """
        # endomorphism or not, this is equivalent to invertibility of
        #   the matrix representation, so any test of this will suffice
        m = self.matrix()
        if not m.is_square():
            return False
        return m.rank() == m.ncols()

    def _latex_(self):
        r"""
        A LaTeX representation of this vector space morphism.

        EXAMPLES::

            sage: H = Hom(QQ^3, QQ^2)
            sage: f = H(matrix(3, 2, range(6)))
            sage: f._latex_().split(' ')
            ['\\text{vector', 'space', 'morphism', 'from',
            '}\n\\Bold{Q}^{3}\\text{', 'to', '}\n\\Bold{Q}^{2}\\text{',
            'represented', 'by', 'the', 'matrix',
            '}\n\\left(\\begin{array}{rr}\n0', '&', '1',
            '\\\\\n2', '&', '3', '\\\\\n4', '&', '5\n\\end{array}\\right)']
        """
        from sage.misc.latex import latex
        s = ('\\text{vector space morphism from }\n', self.domain()._latex_(),
             '\\text{ to }\n', self.codomain()._latex_(),
             '\\text{ represented by the matrix }\n', self.matrix()._latex_())
        return ''.join(s)

    def _repr_(self):
        r"""
        A text representation of this vector space morphism.

        EXAMPLES::

            sage: H = Hom(QQ^3, QQ^2)
            sage: f = H(matrix(3, 2, range(6)))
            sage: f._repr_().split(' ')
            ['Vector', 'space', 'morphism', 'represented', 'by',
            'the', 'matrix:\n[0', '1]\n[2', '3]\n[4', '5]\nDomain:',
            'Vector', 'space', 'of', 'dimension', '3', 'over',
            'Rational', 'Field\nCodomain:', 'Vector', 'space', 'of',
            'dimension', '2', 'over', 'Rational', 'Field']
        """
        m = self.matrix()
        msg = ("Vector space morphism represented by the matrix:\n",
               "{!r}\n",
               "Domain: {}\n",
               "Codomain: {}")
        return ''.join(msg).format(m, self.domain(), self.codomain())
