from typing import Any, Set

from functools import reduce
from itertools import permutations

from sympy.combinatorics import Permutation
from sympy.core import (
    Basic, Expr, Function, diff,
    Pow, Mul, Add, Atom, Lambda, S, Tuple, Dict
)
from sympy.core.cache import cacheit

from sympy.core.symbol import Symbol, Dummy
from sympy.core.symbol import Str
from sympy.core.sympify import _sympify
from sympy.functions import factorial
from sympy.matrices import ImmutableDenseMatrix as Matrix
from sympy.simplify import simplify
from sympy.solvers import solve

from sympy.utilities.exceptions import SymPyDeprecationWarning

# TODO you are a bit excessive in the use of Dummies
# TODO dummy point, literal field
# TODO too often one needs to call doit or simplify on the output, check the
# tests and find out why
from sympy.tensor.array import ImmutableDenseNDimArray

class Manifold(Atom):
    """A mathematical manifold.

    Explanation
    ===========

    A manifold is a topological space that locally resembles
    Euclidean space near each point [1].

    This class does not provide any means to study the topological
    characteristics of the manifold that it represents, though.

    Parameters
    ==========

    name : str
        The name of the manifold.

    dim : int
        The dimension of the manifold.

    Examples
    ========

    >>> from sympy.diffgeom import Manifold
    >>> m = Manifold('M', 2)
    >>> m
    M
    >>> m.dim
    2

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Manifold

    """

    def __new__(cls, name, dim, **kwargs):
        if not isinstance(name, Str):
            name = Str(name)
        dim = _sympify(dim)
        obj = super().__new__(cls, name, dim)

        obj.patches = _deprecated_list(
            "Manifold.patches",
            "external container for registry",
            19321,
            "1.7",
            []
        )
        return obj

    @property
    def name(self):
        return self.args[0]

    @property
    def dim(self):
        return self.args[1]

class Patch(Atom):
    """A patch on a manifold.

    Explanation
    ===========

    Coordinate patch, or patch in short, is a simply-connected open set around a point
    in the manifold [1]. On a manifold one can have many patches that do not always
    include the whole manifold. On these patches coordinate charts can be defined that
    permit the parameterization of any point on the patch in terms of a tuple of
    real numbers (the coordinates).

    This class does not provide any means to study the topological
    characteristics of the patch that it represents.

    Parameters
    ==========

    name : str
        The name of the patch.

    manifold : Manifold
        The manifold on which the patch is defined.

    Examples
    ========

    >>> from sympy.diffgeom import Manifold, Patch
    >>> m = Manifold('M', 2)
    >>> p = Patch('P', m)
    >>> p
    P
    >>> p.dim
    2

    References
    ==========

    .. [1] G. Sussman, J. Wisdom, W. Farr, Functional Differential Geometry (2013)

    """
    def __new__(cls, name, manifold, **kwargs):
        if not isinstance(name, Str):
            name = Str(name)
        obj = super().__new__(cls, name, manifold)

        obj.manifold.patches.append(obj) # deprecated
        obj.coord_systems = _deprecated_list(
            "Patch.coord_systems",
            "external container for registry",
            19321,
            "1.7",
            []
        )
        return obj

    @property
    def name(self):
        return self.args[0]

    @property
    def manifold(self):
        return self.args[1]

    @property
    def dim(self):
        return self.manifold.dim

class CoordSystem(Atom):
    """A coordinate system defined on the patch.

    Explanation
    ===========

    Coordinate system is a system that uses one or more coordinates to uniquely determine
    the position of the points or other geometric elements on a manifold [1].

    By passing Symbols to *symbols* parameter, user can define the name and assumptions
    of coordinate symbols of the coordinate system. If not passed, these symbols are
    generated automatically and are assumed to be real valued.
    By passing *relations* parameter, user can define the tranform relations of coordinate
    systems. Inverse transformation and indirect transformation can be found automatically.
    If this parameter is not passed, coordinate transformation cannot be done.

    Parameters
    ==========

    name : str
        The name of the coordinate system.

    patch : Patch
        The patch where the coordinate system is defined.

    symbols : list of Symbols, optional
        Defines the names and assumptions of coordinate symbols.

    relations : dict, optional
        - key : tuple of two strings, who are the names of systems where
            the coordinates transform from and transform to.
        - value : Lambda returning the transformed coordinates.

    Examples
    ========

    >>> from sympy import symbols, pi, Lambda, Matrix, sqrt, atan2, cos, sin
    >>> from sympy.diffgeom import Manifold, Patch, CoordSystem
    >>> m = Manifold('M', 2)
    >>> p = Patch('P', m)

    >>> x, y = symbols('x y', real=True)
    >>> r, theta = symbols('r theta', nonnegative=True)
    >>> relation_dict = {
    ... ('Car2D', 'Pol'): Lambda((x, y), Matrix([sqrt(x**2 + y**2), atan2(y, x)])),
    ... ('Pol', 'Car2D'): Lambda((r, theta), Matrix([r*cos(theta), r*sin(theta)]))
    ... }
    >>> Car2D = CoordSystem('Car2D', p, [x, y], relation_dict)
    >>> Pol = CoordSystem('Pol', p, [r, theta], relation_dict)

    >>> Car2D
    Car2D
    >>> Car2D.dim
    2
    >>> Car2D.symbols
    [x, y]
    >>> Car2D.transformation(Pol)
    Lambda((x, y), Matrix([
    [sqrt(x**2 + y**2)],
    [      atan2(y, x)]]))

    >>> Car2D.transform(Pol)
    Matrix([
    [sqrt(x**2 + y**2)],
    [      atan2(y, x)]])
    >>> Car2D.transform(Pol, [1, 2])
    Matrix([
    [sqrt(5)],
    [atan(2)]])

    >>> Pol.jacobian(Car2D)
    Matrix([
    [cos(theta), -r*sin(theta)],
    [sin(theta),  r*cos(theta)]])
    >>> Pol.jacobian(Car2D, [1, pi/2])
    Matrix([
    [0, -1],
    [1,  0]])

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Coordinate_system

    """
    def __new__(cls, name, patch, symbols=None, relations={}, **kwargs):
        if not isinstance(name, Str):
            name = Str(name)

        # canonicallize the symbols
        if symbols is None:
            names = kwargs.get('names', None)
            if names is None:
                symbols = Tuple(
                    *[Symbol('%s_%s' % (name.name, i), real=True) for i in range(patch.dim)]
                )
            else:
                SymPyDeprecationWarning(
                    feature="Class signature 'names' of CoordSystem",
                    useinstead="class signature 'symbols'",
                    issue=19321,
                    deprecated_since_version="1.7"
                ).warn()
                symbols = Tuple(
                    *[Symbol(n, real=True) for n in names]
                )
        else:
            syms = []
            for s in symbols:
                if isinstance(s, Symbol):
                    syms.append(Symbol(s.name, **s._assumptions.generator))
                elif isinstance(s, str):
                    SymPyDeprecationWarning(
                        feature="Passing str as coordinate symbol's name",
                        useinstead="Symbol which contains the name and assumption for coordinate symbol",
                        issue=19321,
                        deprecated_since_version="1.7"
                    ).warn()
                    syms.append(Symbol(s, real=True))
            symbols = Tuple(*syms)

        # canonicallize the relations
        rel_temp = {}
        for k,v in relations.items():
            s1, s2 = k
            if not isinstance(s1, Str):
                s1 = Str(s1)
            if not isinstance(s2, Str):
                s2 = Str(s2)
            key = Tuple(s1, s2)
            rel_temp[key] = v
        relations = Dict(rel_temp)

        # construct the object
        obj = super().__new__(cls, name, patch, symbols, relations)

        # Add deprecated attributes
        obj.transforms = _deprecated_dict(
            "Mutable CoordSystem.transforms",
            "'relations' parameter in class signature",
            19321,
            "1.7",
            {}
        )
        obj._names = [str(n) for n in symbols]
        obj.patch.coord_systems.append(obj) # deprecated
        obj._dummies = [Dummy(str(n)) for n in symbols] # deprecated
        obj._dummy = Dummy()

        return obj

    @property
    def name(self):
        return self.args[0]

    @property
    def patch(self):
        return self.args[1]

    @property
    def manifold(self):
        return self.patch.manifold

    @property
    def symbols(self):
        return [
            CoordinateSymbol(
                self, i, **s._assumptions.generator
            ) for i,s in enumerate(self.args[2])
        ]

    @property
    def relations(self):
        return self.args[3]

    @property
    def dim(self):
        return self.patch.dim

    ##########################################################################
    # Finding transformation relation
    ##########################################################################

    def transformation(self, sys):
        """
        Return coordinate transform relation from *self* to *sys* as Lambda.
        """
        if self.relations != sys.relations:
            raise TypeError(
        "Two coordinate systems have different relations")

        key = Tuple(self.name, sys.name)
        if key in self.relations:
            return self.relations[key]
        elif key[::-1] in self.relations:
            return self._inverse_transformation(sys, self)
        else:
            return self._indirect_transformation(self, sys)

    @staticmethod
    def _inverse_transformation(sys1, sys2):
        # Find the transformation relation from sys2 to sys1
        forward_transform = sys1.transform(sys2)
        forward_syms, forward_results = forward_transform.args

        inv_syms = [i.as_dummy() for i in forward_syms]
        inv_results = solve(
            [t[0] - t[1] for t in zip(inv_syms, forward_results)],
            list(forward_syms), dict=True)[0]
        inv_results = [inv_results[s] for s in forward_syms]

        signature = tuple(inv_syms)
        expr = Matrix(inv_results)
        return Lambda(signature, expr)

    @classmethod
    @cacheit
    def _indirect_transformation(cls, sys1, sys2):
        # Find the transformation relation between two indirectly connected coordinate systems
        path = cls._dijkstra(sys1, sys2)
        Lambdas = []
        for i in range(len(path) - 1):
            s1, s2 = path[i], path[i + 1]
            Lambdas.append(s1.transformation(s2))
        syms = Lambdas[-1].signature
        expr = syms
        for l in reversed(Lambdas):
            expr = l(*expr)
        return Lambda(syms, expr)

    @staticmethod
    def _dijkstra(sys1, sys2):
        # Use Dijkstra algorithm to find the shortest path between two indirectly-connected
        # coordinate systems
        relations = sys1.relations
        graph = {}
        for s1, s2 in relations.keys():
            if s1 not in graph:
                graph[s1] = {s2}
            else:
                graph[s1].add(s2)
            if s2 not in graph:
                graph[s2] = {s1}
            else:
                graph[s2].add(s1)

        path_dict = {sys:[0, [], 0] for sys in graph} # minimum distance, path, times of visited

        def visit(sys):
            path_dict[sys][2] = 1
            for newsys in graph[sys]:
                distance = path_dict[sys][0] + 1
                if path_dict[newsys][0] >= distance or not path_dict[newsys][1]:
                    path_dict[newsys][0] = distance
                    path_dict[newsys][1] = [i for i in path_dict[sys][1]]
                    path_dict[newsys][1].append(sys)

        visit(sys1)

        while True:
            min_distance = max(path_dict.values(), key=lambda x:x[0])[0]
            newsys = None
            for sys, lst in path_dict.items():
                if 0 < lst[0] <= min_distance and not lst[2]:
                    min_distance = lst[0]
                    newsys = sys
            if newsys is None:
                break
            visit(newsys)

        result = path_dict[sys2][1]
        result.append(sys2)

        if result == [sys2]:
            raise KeyError("Two coordinate systems are not connected.")
        return result

    def connect_to(self, to_sys, from_coords, to_exprs, inverse=True, fill_in_gaps=False):
        SymPyDeprecationWarning(
            feature="CoordSystem.connect_to",
            useinstead="new instance generated with new 'transforms' parameter",
            issue=19321,
            deprecated_since_version="1.7"
        ).warn()

        from_coords, to_exprs = dummyfy(from_coords, to_exprs)
        self.transforms[to_sys] = Matrix(from_coords), Matrix(to_exprs)

        if inverse:
            to_sys.transforms[self] = self._inv_transf(from_coords, to_exprs)

        if fill_in_gaps:
            self._fill_gaps_in_transformations()

    @staticmethod
    def _inv_transf(from_coords, to_exprs):
        # Will be removed when connect_to is removed
        inv_from = [i.as_dummy() for i in from_coords]
        inv_to = solve(
            [t[0] - t[1] for t in zip(inv_from, to_exprs)],
            list(from_coords), dict=True)[0]
        inv_to = [inv_to[fc] for fc in from_coords]
        return Matrix(inv_from), Matrix(inv_to)

    @staticmethod
    def _fill_gaps_in_transformations():
        # Will be removed when connect_to is removed
        raise NotImplementedError

    ##########################################################################
    # Coordinate transformations
    ##########################################################################

    def transform(self, sys, coordinates=None):
        """
        Return the result of coordinate transformation from *self* to *sys*.
        If coordinates are not given, coordinate symbols of *self* are used.
        """
        if coordinates is None:
            coordinates = Matrix(self.symbols)
        else:
            coordinates = Matrix(coordinates)
        if self != sys:
            transf = self.transformation(sys)
            coordinates = transf(*coordinates)
        return coordinates

    def coord_tuple_transform_to(self, to_sys, coords):
        """Transform ``coords`` to coord system ``to_sys``."""
        SymPyDeprecationWarning(
            feature="CoordSystem.coord_tuple_transform_to",
            useinstead="CoordSystem.transform",
            issue=19321,
            deprecated_since_version="1.7"
        ).warn()

        coords = Matrix(coords)
        if self != to_sys:
            transf = self.transforms[to_sys]
            coords = transf[1].subs(list(zip(transf[0], coords)))
        return coords

    def jacobian(self, sys, coordinates=None):
        """
        Return the jacobian matrix of a transformation.
        """
        result = self.transform(sys).jacobian(self.symbols)
        if coordinates is not None:
            result = result.subs(list(zip(self.symbols, coordinates)))
        return result
    jacobian_matrix = jacobian

    def jacobian_determinant(self, sys, coordinates=None):
        """Return the jacobian determinant of a transformation."""
        return self.jacobian(sys, coordinates).det()


    ##########################################################################
    # Points
    ##########################################################################

    def point(self, coords):
        """Create a ``Point`` with coordinates given in this coord system."""
        return Point(self, coords)

    def point_to_coords(self, point):
        """Calculate the coordinates of a point in this coord system."""
        return point.coords(self)

    ##########################################################################
    # Base fields.
    ##########################################################################

    def base_scalar(self, coord_index):
        """Return ``BaseScalarField`` that takes a point and returns one of the coordinates."""
        return BaseScalarField(self, coord_index)
    coord_function = base_scalar

    def base_scalars(self):
        """Returns a list of all coordinate functions.
        For more details see the ``base_scalar`` method of this class."""
        return [self.base_scalar(i) for i in range(self.dim)]
    coord_functions = base_scalars

    def base_vector(self, coord_index):
        """Return a basis vector field.
        The basis vector field for this coordinate system. It is also an
        operator on scalar fields."""
        return BaseVectorField(self, coord_index)

    def base_vectors(self):
        """Returns a list of all base vectors.
        For more details see the ``base_vector`` method of this class."""
        return [self.base_vector(i) for i in range(self.dim)]

    def base_oneform(self, coord_index):
        """Return a basis 1-form field.
        The basis one-form field for this coordinate system. It is also an
        operator on vector fields."""
        return Differential(self.coord_function(coord_index))

    def base_oneforms(self):
        """Returns a list of all base oneforms.
        For more details see the ``base_oneform`` method of this class."""
        return [self.base_oneform(i) for i in range(self.dim)]

class CoordinateSymbol(Symbol):
    """A symbol which denotes an abstract value of i-th coordinate of
    the coordinate system with given context.

    Explanation
    ===========

    Each coordinates in coordinate system are represented by unique symbol,
    such as x, y, z in Cartesian coordinate system.

    You may not construct this class directly. Instead, use `symbols` method
    of CoordSystem.

    Parameters
    ==========

    coord_sys : CoordSystem

    index : integer

    Examples
    ========

    >>> from sympy import symbols
    >>> from sympy.diffgeom import Manifold, Patch, CoordSystem
    >>> m = Manifold('M', 2)
    >>> p = Patch('P', m)
    >>> _x, _y = symbols('x y', nonnegative=True)

    >>> C = CoordSystem('C', p, [_x, _y])
    >>> x, y = C.symbols

    >>> x.name
    'x'
    >>> x.coord_sys == C
    True
    >>> x.index
    0
    >>> x.is_nonnegative
    True

    """
    def __new__(cls, coord_sys, index, **assumptions):
        name = coord_sys.args[2][index].name
        obj = super().__new__(cls, name, **assumptions)
        obj.coord_sys = coord_sys
        obj.index = index
        return obj

    def __getnewargs__(self):
        return (self.coord_sys, self.index)

    def _hashable_content(self):
        return (
            self.coord_sys, self.index
        ) + tuple(sorted(self.assumptions0.items()))

class Point(Basic):
    """Point defined in a coordinate system.

    Explanation
    ===========

    Mathematically, point is defined in the manifold and does not have any coordinates
    by itself. Coordinate system is what imbues the coordinates to the point by coordinate
    chart. However, due to the difficulty of realizing such logic, you must supply
    a coordinate system and coordinates to define a Point here.

    The usage of this object after its definition is independent of the
    coordinate system that was used in order to define it, however due to
    limitations in the simplification routines you can arrive at complicated
    expressions if you use inappropriate coordinate systems.

    Parameters
    ==========

    coord_sys : CoordSystem

    coords : list
        The coordinates of the point.

    Examples
    ========

    >>> from sympy import pi
    >>> from sympy.diffgeom import Point
    >>> from sympy.diffgeom.rn import R2, R2_r, R2_p
    >>> rho, theta = R2_p.symbols

    >>> p = Point(R2_p, [rho, 3*pi/4])

    >>> p.manifold == R2
    True

    >>> p.coords()
    Matrix([
    [   rho],
    [3*pi/4]])
    >>> p.coords(R2_r)
    Matrix([
    [-sqrt(2)*rho/2],
    [ sqrt(2)*rho/2]])

    """

    def __new__(cls, coord_sys, coords, **kwargs):
        coords = Matrix(coords)
        obj = super().__new__(cls, coord_sys, coords)
        obj._coord_sys = coord_sys
        obj._coords = coords
        return obj

    @property
    def patch(self):
        return self._coord_sys.patch

    @property
    def manifold(self):
        return self._coord_sys.manifold

    @property
    def dim(self):
        return self.manifold.dim

    def coords(self, sys=None):
        """
        Coordinates of the point in given coordinate system. If coordinate system
        is not passed, it returns the coordinates in the coordinate system in which
        the poin was defined.
        """
        if sys is None:
            return self._coords
        else:
            return self._coord_sys.transform(sys, self._coords)

    @property
    def free_symbols(self):
        return self._coords.free_symbols

class BaseScalarField(Expr):
    """Base scalar field over a manifold for a given coordinate system.

    Explanation
    ===========

    A scalar field takes a point as an argument and returns a scalar.
    A base scalar field of a coordinate system takes a point and returns one of
    the coordinates of that point in the coordinate system in question.

    To define a scalar field you need to choose the coordinate system and the
    index of the coordinate.

    The use of the scalar field after its definition is independent of the
    coordinate system in which it was defined, however due to limitations in
    the simplification routines you may arrive at more complicated
    expression if you use unappropriate coordinate systems.
    You can build complicated scalar fields by just building up SymPy
    expressions containing ``BaseScalarField`` instances.

    Parameters
    ==========

    coord_sys : CoordSystem

    index : integer

    Examples
    ========

    >>> from sympy import Function, pi
    >>> from sympy.diffgeom import BaseScalarField
    >>> from sympy.diffgeom.rn import R2_r, R2_p
    >>> rho, _ = R2_p.symbols
    >>> point = R2_p.point([rho, 0])
    >>> fx, fy = R2_r.base_scalars()
    >>> ftheta = BaseScalarField(R2_r, 1)

    >>> fx(point)
    rho
    >>> fy(point)
    0

    >>> (fx**2+fy**2).rcall(point)
    rho**2

    >>> g = Function('g')
    >>> fg = g(ftheta-pi)
    >>> fg.rcall(point)
    g(-pi)

    """

    is_commutative = True

    def __new__(cls, coord_sys, index, **kwargs):
        index = _sympify(index)
        obj = super().__new__(cls, coord_sys, index)
        obj._coord_sys = coord_sys
        obj._index = index
        return obj

    @property
    def coord_sys(self):
        return self.args[0]

    @property
    def index(self):
        return self.args[1]

    @property
    def patch(self):
        return self.coord_sys.patch

    @property
    def manifold(self):
        return self.coord_sys.manifold

    @property
    def dim(self):
        return self.manifold.dim

    def __call__(self, *args):
        """Evaluating the field at a point or doing nothing.
        If the argument is a ``Point`` instance, the field is evaluated at that
        point. The field is returned itself if the argument is any other
        object. It is so in order to have working recursive calling mechanics
        for all fields (check the ``__call__`` method of ``Expr``).
        """
        point = args[0]
        if len(args) != 1 or not isinstance(point, Point):
            return self
        coords = point.coords(self._coord_sys)
        # XXX Calling doit  is necessary with all the Subs expressions
        # XXX Calling simplify is necessary with all the trig expressions
        return simplify(coords[self._index]).doit()

    # XXX Workaround for limitations on the content of args
    free_symbols = set()  # type: Set[Any]

    def doit(self):
        return self


class BaseVectorField(Expr):
    r"""Base vector field over a manifold for a given coordinate system.

    Explanation
    ===========

    A vector field is an operator taking a scalar field and returning a
    directional derivative (which is also a scalar field).
    A base vector field is the same type of operator, however the derivation is
    specifically done with respect to a chosen coordinate.

    To define a base vector field you need to choose the coordinate system and
    the index of the coordinate.

    The use of the vector field after its definition is independent of the
    coordinate system in which it was defined, however due to limitations in the
    simplification routines you may arrive at more complicated expression if you
    use unappropriate coordinate systems.

    Parameters
    ==========
    coord_sys : CoordSystem

    index : integer

    Examples
    ========

    >>> from sympy import Function
    >>> from sympy.diffgeom.rn import R2_p, R2_r
    >>> from sympy.diffgeom import BaseVectorField
    >>> from sympy import pprint

    >>> x, y = R2_r.symbols
    >>> rho, theta = R2_p.symbols
    >>> fx, fy = R2_r.base_scalars()
    >>> point_p = R2_p.point([rho, theta])
    >>> point_r = R2_r.point([x, y])

    >>> g = Function('g')
    >>> s_field = g(fx, fy)

    >>> v = BaseVectorField(R2_r, 1)
    >>> pprint(v(s_field))
    / d           \|
    |---(g(x, xi))||
    \dxi          /|xi=y
    >>> pprint(v(s_field).rcall(point_r).doit())
    d
    --(g(x, y))
    dy
    >>> pprint(v(s_field).rcall(point_p))
    / d                        \|
    |---(g(rho*cos(theta), xi))||
    \dxi                       /|xi=rho*sin(theta)

    """

    is_commutative = False

    def __new__(cls, coord_sys, index, **kwargs):
        index = _sympify(index)
        obj = super().__new__(cls, coord_sys, index)
        obj._coord_sys = coord_sys
        obj._index = index
        return obj

    @property
    def coord_sys(self):
        return self.args[0]

    @property
    def index(self):
        return self.args[1]

    @property
    def patch(self):
        return self.coord_sys.patch

    @property
    def manifold(self):
        return self.coord_sys.manifold

    @property
    def dim(self):
        return self.manifold.dim

    def __call__(self, scalar_field):
        """Apply on a scalar field.
        The action of a vector field on a scalar field is a directional
        differentiation.
        If the argument is not a scalar field an error is raised.
        """
        if covariant_order(scalar_field) or contravariant_order(scalar_field):
            raise ValueError('Only scalar fields can be supplied as arguments to vector fields.')

        if scalar_field is None:
            return self

        base_scalars = list(scalar_field.atoms(BaseScalarField))

        # First step: e_x(x+r**2) -> e_x(x) + 2*r*e_x(r)
        d_var = self._coord_sys._dummy
        # TODO: you need a real dummy function for the next line
        d_funcs = [Function('_#_%s' % i)(d_var) for i,
                   b in enumerate(base_scalars)]
        d_result = scalar_field.subs(list(zip(base_scalars, d_funcs)))
        d_result = d_result.diff(d_var)

        # Second step: e_x(x) -> 1 and e_x(r) -> cos(atan2(x, y))
        coords = self._coord_sys.symbols
        d_funcs_deriv = [f.diff(d_var) for f in d_funcs]
        d_funcs_deriv_sub = []
        for b in base_scalars:
            jac = self._coord_sys.jacobian(b._coord_sys, coords)
            d_funcs_deriv_sub.append(jac[b._index, self._index])
        d_result = d_result.subs(list(zip(d_funcs_deriv, d_funcs_deriv_sub)))

        # Remove the dummies
        result = d_result.subs(list(zip(d_funcs, base_scalars)))
        result = result.subs(list(zip(coords, self._coord_sys.coord_functions())))
        return result.doit()

def _find_coords(expr):
    # Finds CoordinateSystems existing in expr
    fields = expr.atoms(BaseScalarField, BaseVectorField)
    result = set()
    for f in fields:
        result.add(f._coord_sys)
    return result

class Commutator(Expr):
    r"""Commutator of two vector fields.

    Explanation
    ===========

    The commutator of two vector fields `v_1` and `v_2` is defined as the
    vector field `[v_1, v_2]` that evaluated on each scalar field `f` is equal
    to `v_1(v_2(f)) - v_2(v_1(f))`.

    Examples
    ========


    >>> from sympy.diffgeom.rn import R2_p, R2_r
    >>> from sympy.diffgeom import Commutator
    >>> from sympy.simplify import simplify

    >>> fx, fy = R2_r.base_scalars()
    >>> e_x, e_y = R2_r.base_vectors()
    >>> e_r = R2_p.base_vector(0)

    >>> c_xy = Commutator(e_x, e_y)
    >>> c_xr = Commutator(e_x, e_r)
    >>> c_xy
    0

    Unfortunately, the current code is not able to compute everything:

    >>> c_xr
    Commutator(e_x, e_rho)
    >>> simplify(c_xr(fy**2))
    -2*cos(theta)*y**2/(x**2 + y**2)

    """
    def __new__(cls, v1, v2):
        if (covariant_order(v1) or contravariant_order(v1) != 1
                or covariant_order(v2) or contravariant_order(v2) != 1):
            raise ValueError(
                'Only commutators of vector fields are supported.')
        if v1 == v2:
            return S.Zero
        coord_sys = set().union(*[_find_coords(v) for v in (v1, v2)])
        if len(coord_sys) == 1:
            # Only one coordinate systems is used, hence it is easy enough to
            # actually evaluate the commutator.
            if all(isinstance(v, BaseVectorField) for v in (v1, v2)):
                return S.Zero
            bases_1, bases_2 = [list(v.atoms(BaseVectorField))
                                for v in (v1, v2)]
            coeffs_1 = [v1.expand().coeff(b) for b in bases_1]
            coeffs_2 = [v2.expand().coeff(b) for b in bases_2]
            res = 0
            for c1, b1 in zip(coeffs_1, bases_1):
                for c2, b2 in zip(coeffs_2, bases_2):
                    res += c1*b1(c2)*b2 - c2*b2(c1)*b1
            return res
        else:
            obj = super().__new__(cls, v1, v2)
            obj._v1 = v1 # deprecated assignment
            obj._v2 = v2 # deprecated assignment
            return obj

    @property
    def v1(self):
        return self.args[0]

    @property
    def v2(self):
        return self.args[1]

    def __call__(self, scalar_field):
        """Apply on a scalar field.
        If the argument is not a scalar field an error is raised.
        """
        return self.v1(self.v2(scalar_field)) - self.v2(self.v1(scalar_field))


class Differential(Expr):
    r"""Return the differential (exterior derivative) of a form field.

    Explanation
    ===========

    The differential of a form (i.e. the exterior derivative) has a complicated
    definition in the general case.
    The differential `df` of the 0-form `f` is defined for any vector field `v`
    as `df(v) = v(f)`.

    Examples
    ========

    >>> from sympy import Function
    >>> from sympy.diffgeom.rn import R2_r
    >>> from sympy.diffgeom import Differential
    >>> from sympy import pprint

    >>> fx, fy = R2_r.base_scalars()
    >>> e_x, e_y = R2_r.base_vectors()
    >>> g = Function('g')
    >>> s_field = g(fx, fy)
    >>> dg = Differential(s_field)

    >>> dg
    d(g(x, y))
    >>> pprint(dg(e_x))
    / d           \|
    |---(g(xi, y))||
    \dxi          /|xi=x
    >>> pprint(dg(e_y))
    / d           \|
    |---(g(x, xi))||
    \dxi          /|xi=y

    Applying the exterior derivative operator twice always results in:

    >>> Differential(dg)
    0
    """

    is_commutative = False

    def __new__(cls, form_field):
        if contravariant_order(form_field):
            raise ValueError(
                'A vector field was supplied as an argument to Differential.')
        if isinstance(form_field, Differential):
            return S.Zero
        else:
            obj = super().__new__(cls, form_field)
            obj._form_field = form_field # deprecated assignment
            return obj

    @property
    def form_field(self):
        return self.args[0]

    def __call__(self, *vector_fields):
        """Apply on a list of vector_fields.

        Explanation
        ===========

        If the number of vector fields supplied is not equal to 1 + the order of
        the form field inside the differential the result is undefined.

        For 1-forms (i.e. differentials of scalar fields) the evaluation is
        done as `df(v)=v(f)`. However if `v` is ``None`` instead of a vector
        field, the differential is returned unchanged. This is done in order to
        permit partial contractions for higher forms.

        In the general case the evaluation is done by applying the form field
        inside the differential on a list with one less elements than the number
        of elements in the original list. Lowering the number of vector fields
        is achieved through replacing each pair of fields by their
        commutator.

        If the arguments are not vectors or ``None``s an error is raised.
        """
        if any((contravariant_order(a) != 1 or covariant_order(a)) and a is not None
                for a in vector_fields):
            raise ValueError('The arguments supplied to Differential should be vector fields or Nones.')
        k = len(vector_fields)
        if k == 1:
            if vector_fields[0]:
                return vector_fields[0].rcall(self._form_field)
            return self
        else:
            # For higher form it is more complicated:
            # Invariant formula:
            # https://en.wikipedia.org/wiki/Exterior_derivative#Invariant_formula
            # df(v1, ... vn) = +/- vi(f(v1..no i..vn))
            #                  +/- f([vi,vj],v1..no i, no j..vn)
            f = self._form_field
            v = vector_fields
            ret = 0
            for i in range(k):
                t = v[i].rcall(f.rcall(*v[:i] + v[i + 1:]))
                ret += (-1)**i*t
                for j in range(i + 1, k):
                    c = Commutator(v[i], v[j])
                    if c:  # TODO this is ugly - the Commutator can be Zero and
                        # this causes the next line to fail
                        t = f.rcall(*(c,) + v[:i] + v[i + 1:j] + v[j + 1:])
                        ret += (-1)**(i + j)*t
            return ret

class TensorProduct(Expr):
    """Tensor product of forms.

    Explanation
    ===========

    The tensor product permits the creation of multilinear functionals (i.e.
    higher order tensors) out of lower order fields (e.g. 1-forms and vector
    fields). However, the higher tensors thus created lack the interesting
    features provided by the other type of product, the wedge product, namely
    they are not antisymmetric and hence are not form fields.

    Examples
    ========

    >>> from sympy.diffgeom.rn import R2_r
    >>> from sympy.diffgeom import TensorProduct

    >>> fx, fy = R2_r.base_scalars()
    >>> e_x, e_y = R2_r.base_vectors()
    >>> dx, dy = R2_r.base_oneforms()

    >>> TensorProduct(dx, dy)(e_x, e_y)
    1
    >>> TensorProduct(dx, dy)(e_y, e_x)
    0
    >>> TensorProduct(dx, fx*dy)(fx*e_x, e_y)
    x**2
    >>> TensorProduct(e_x, e_y)(fx**2, fy**2)
    4*x*y
    >>> TensorProduct(e_y, dx)(fy)
    dx

    You can nest tensor products.

    >>> tp1 = TensorProduct(dx, dy)
    >>> TensorProduct(tp1, dx)(e_x, e_y, e_x)
    1

    You can make partial contraction for instance when 'raising an index'.
    Putting ``None`` in the second argument of ``rcall`` means that the
    respective position in the tensor product is left as it is.

    >>> TP = TensorProduct
    >>> metric = TP(dx, dx) + 3*TP(dy, dy)
    >>> metric.rcall(e_y, None)
    3*dy

    Or automatically pad the args with ``None`` without specifying them.

    >>> metric.rcall(e_y)
    3*dy

    """
    def __new__(cls, *args):
        scalar = Mul(*[m for m in args if covariant_order(m) + contravariant_order(m) == 0])
        multifields = [m for m in args if covariant_order(m) + contravariant_order(m)]
        if multifields:
            if len(multifields) == 1:
                return scalar*multifields[0]
            return scalar*super().__new__(cls, *multifields)
        else:
            return scalar

    def __call__(self, *fields):
        """Apply on a list of fields.

        If the number of input fields supplied is not equal to the order of
        the tensor product field, the list of arguments is padded with ``None``'s.

        The list of arguments is divided in sublists depending on the order of
        the forms inside the tensor product. The sublists are provided as
        arguments to these forms and the resulting expressions are given to the
        constructor of ``TensorProduct``.

        """
        tot_order = covariant_order(self) + contravariant_order(self)
        tot_args = len(fields)
        if tot_args != tot_order:
            fields = list(fields) + [None]*(tot_order - tot_args)
        orders = [covariant_order(f) + contravariant_order(f) for f in self._args]
        indices = [sum(orders[:i + 1]) for i in range(len(orders) - 1)]
        fields = [fields[i:j] for i, j in zip([0] + indices, indices + [None])]
        multipliers = [t[0].rcall(*t[1]) for t in zip(self._args, fields)]
        return TensorProduct(*multipliers)


class WedgeProduct(TensorProduct):
    """Wedge product of forms.

    Explanation
    ===========

    In the context of integration only completely antisymmetric forms make
    sense. The wedge product permits the creation of such forms.

    Examples
    ========

    >>> from sympy.diffgeom.rn import R2_r
    >>> from sympy.diffgeom import WedgeProduct

    >>> fx, fy = R2_r.base_scalars()
    >>> e_x, e_y = R2_r.base_vectors()
    >>> dx, dy = R2_r.base_oneforms()

    >>> WedgeProduct(dx, dy)(e_x, e_y)
    1
    >>> WedgeProduct(dx, dy)(e_y, e_x)
    -1
    >>> WedgeProduct(dx, fx*dy)(fx*e_x, e_y)
    x**2
    >>> WedgeProduct(e_x, e_y)(fy, None)
    -e_x

    You can nest wedge products.

    >>> wp1 = WedgeProduct(dx, dy)
    >>> WedgeProduct(wp1, dx)(e_x, e_y, e_x)
    0

    """
    # TODO the calculation of signatures is slow
    # TODO you do not need all these permutations (neither the prefactor)
    def __call__(self, *fields):
        """Apply on a list of vector_fields.
        The expression is rewritten internally in terms of tensor products and evaluated."""
        orders = (covariant_order(e) + contravariant_order(e) for e in self.args)
        mul = 1/Mul(*(factorial(o) for o in orders))
        perms = permutations(fields)
        perms_par = (Permutation(
            p).signature() for p in permutations(list(range(len(fields)))))
        tensor_prod = TensorProduct(*self.args)
        return mul*Add(*[tensor_prod(*p[0])*p[1] for p in zip(perms, perms_par)])


class LieDerivative(Expr):
    """Lie derivative with respect to a vector field.

    Explanation
    ===========

    The transport operator that defines the Lie derivative is the pushforward of
    the field to be derived along the integral curve of the field with respect
    to which one derives.

    Examples
    ========

    >>> from sympy.diffgeom.rn import R2_r, R2_p
    >>> from sympy.diffgeom import (LieDerivative, TensorProduct)

    >>> fx, fy = R2_r.base_scalars()
    >>> e_x, e_y = R2_r.base_vectors()
    >>> e_rho, e_theta = R2_p.base_vectors()
    >>> dx, dy = R2_r.base_oneforms()

    >>> LieDerivative(e_x, fy)
    0
    >>> LieDerivative(e_x, fx)
    1
    >>> LieDerivative(e_x, e_x)
    0

    The Lie derivative of a tensor field by another tensor field is equal to
    their commutator:

    >>> LieDerivative(e_x, e_rho)
    Commutator(e_x, e_rho)
    >>> LieDerivative(e_x + e_y, fx)
    1

    >>> tp = TensorProduct(dx, dy)
    >>> LieDerivative(e_x, tp)
    LieDerivative(e_x, TensorProduct(dx, dy))
    >>> LieDerivative(e_x, tp)
    LieDerivative(e_x, TensorProduct(dx, dy))

    """
    def __new__(cls, v_field, expr):
        expr_form_ord = covariant_order(expr)
        if contravariant_order(v_field) != 1 or covariant_order(v_field):
            raise ValueError('Lie derivatives are defined only with respect to'
                             ' vector fields. The supplied argument was not a '
                             'vector field.')
        if expr_form_ord > 0:
            obj = super().__new__(cls, v_field, expr)
            # deprecated assignments
            obj._v_field = v_field
            obj._expr = expr
            return obj
        if expr.atoms(BaseVectorField):
            return Commutator(v_field, expr)
        else:
            return v_field.rcall(expr)

    @property
    def v_field(self):
        return self.args[0]

    @property
    def expr(self):
        return self.args[1]

    def __call__(self, *args):
        v = self.v_field
        expr = self.expr
        lead_term = v(expr(*args))
        rest = Add(*[Mul(*args[:i] + (Commutator(v, args[i]),) + args[i + 1:])
                     for i in range(len(args))])
        return lead_term - rest


class BaseCovarDerivativeOp(Expr):
    """Covariant derivative operator with respect to a base vector.

    Examples
    ========

    >>> from sympy.diffgeom.rn import R2_r
    >>> from sympy.diffgeom import BaseCovarDerivativeOp
    >>> from sympy.diffgeom import metric_to_Christoffel_2nd, TensorProduct

    >>> TP = TensorProduct
    >>> fx, fy = R2_r.base_scalars()
    >>> e_x, e_y = R2_r.base_vectors()
    >>> dx, dy = R2_r.base_oneforms()

    >>> ch = metric_to_Christoffel_2nd(TP(dx, dx) + TP(dy, dy))
    >>> ch
    [[[0, 0], [0, 0]], [[0, 0], [0, 0]]]
    >>> cvd = BaseCovarDerivativeOp(R2_r, 0, ch)
    >>> cvd(fx)
    1
    >>> cvd(fx*e_x)
    e_x
    """

    def __new__(cls, coord_sys, index, christoffel):
        index = _sympify(index)
        christoffel = ImmutableDenseNDimArray(christoffel)
        obj = super().__new__(cls, coord_sys, index, christoffel)
        # deprecated assignments
        obj._coord_sys = coord_sys
        obj._index = index
        obj._christoffel = christoffel
        return obj

    @property
    def coord_sys(self):
        return self.args[0]

    @property
    def index(self):
        return self.args[1]

    @property
    def christoffel(self):
        return self.args[2]

    def __call__(self, field):
        """Apply on a scalar field.

        The action of a vector field on a scalar field is a directional
        differentiation.
        If the argument is not a scalar field the behaviour is undefined.
        """
        if covariant_order(field) != 0:
            raise NotImplementedError()

        field = vectors_in_basis(field, self._coord_sys)

        wrt_vector = self._coord_sys.base_vector(self._index)
        wrt_scalar = self._coord_sys.coord_function(self._index)
        vectors = list(field.atoms(BaseVectorField))

        # First step: replace all vectors with something susceptible to
        # derivation and do the derivation
        # TODO: you need a real dummy function for the next line
        d_funcs = [Function('_#_%s' % i)(wrt_scalar) for i,
                   b in enumerate(vectors)]
        d_result = field.subs(list(zip(vectors, d_funcs)))
        d_result = wrt_vector(d_result)

        # Second step: backsubstitute the vectors in
        d_result = d_result.subs(list(zip(d_funcs, vectors)))

        # Third step: evaluate the derivatives of the vectors
        derivs = []
        for v in vectors:
            d = Add(*[(self._christoffel[k, wrt_vector._index, v._index]
                       *v._coord_sys.base_vector(k))
                      for k in range(v._coord_sys.dim)])
            derivs.append(d)
        to_subs = [wrt_vector(d) for d in d_funcs]
        # XXX: This substitution can fail when there are Dummy symbols and the
        # cache is disabled: https://github.com/sympy/sympy/issues/17794
        result = d_result.subs(list(zip(to_subs, derivs)))

        # Remove the dummies
        result = result.subs(list(zip(d_funcs, vectors)))
        return result.doit()


class CovarDerivativeOp(Expr):
    """Covariant derivative operator.

    Examples
    ========

    >>> from sympy.diffgeom.rn import R2_r
    >>> from sympy.diffgeom import CovarDerivativeOp
    >>> from sympy.diffgeom import metric_to_Christoffel_2nd, TensorProduct
    >>> TP = TensorProduct
    >>> fx, fy = R2_r.base_scalars()
    >>> e_x, e_y = R2_r.base_vectors()
    >>> dx, dy = R2_r.base_oneforms()
    >>> ch = metric_to_Christoffel_2nd(TP(dx, dx) + TP(dy, dy))

    >>> ch
    [[[0, 0], [0, 0]], [[0, 0], [0, 0]]]
    >>> cvd = CovarDerivativeOp(fx*e_x, ch)
    >>> cvd(fx)
    x
    >>> cvd(fx*e_x)
    x*e_x

    """

    def __new__(cls, wrt, christoffel):
        if len({v._coord_sys for v in wrt.atoms(BaseVectorField)}) > 1:
            raise NotImplementedError()
        if contravariant_order(wrt) != 1 or covariant_order(wrt):
            raise ValueError('Covariant derivatives are defined only with '
                             'respect to vector fields. The supplied argument '
                             'was not a vector field.')
        obj = super().__new__(cls, wrt, christoffel)
        # deprecated assigments
        obj._wrt = wrt
        obj._christoffel = christoffel
        return obj

    @property
    def wrt(self):
        return self.args[0]

    @property
    def christoffel(self):
        return self.args[1]

    def __call__(self, field):
        vectors = list(self._wrt.atoms(BaseVectorField))
        base_ops = [BaseCovarDerivativeOp(v._coord_sys, v._index, self._christoffel)
                    for v in vectors]
        return self._wrt.subs(list(zip(vectors, base_ops))).rcall(field)


###############################################################################
# Integral curves on vector fields
###############################################################################
def intcurve_series(vector_field, param, start_point, n=6, coord_sys=None, coeffs=False):
    r"""Return the series expansion for an integral curve of the field.

    Explanation
    ===========

    Integral curve is a function `\gamma` taking a parameter in `R` to a point
    in the manifold. It verifies the equation:

    `V(f)\big(\gamma(t)\big) = \frac{d}{dt}f\big(\gamma(t)\big)`

    where the given ``vector_field`` is denoted as `V`. This holds for any
    value `t` for the parameter and any scalar field `f`.

    This equation can also be decomposed of a basis of coordinate functions
    `V(f_i)\big(\gamma(t)\big) = \frac{d}{dt}f_i\big(\gamma(t)\big) \quad \forall i`

    This function returns a series expansion of `\gamma(t)` in terms of the
    coordinate system ``coord_sys``. The equations and expansions are necessarily
    done in coordinate-system-dependent way as there is no other way to
    represent movement between points on the manifold (i.e. there is no such
    thing as a difference of points for a general manifold).

    Parameters
    ==========
    vector_field
        the vector field for which an integral curve will be given

    param
        the argument of the function `\gamma` from R to the curve

    start_point
        the point which corresponds to `\gamma(0)`

    n
        the order to which to expand

    coord_sys
        the coordinate system in which to expand
        coeffs (default False) - if True return a list of elements of the expansion

    Examples
    ========

    Use the predefined R2 manifold:

    >>> from sympy.abc import t, x, y
    >>> from sympy.diffgeom.rn import R2_p, R2_r
    >>> from sympy.diffgeom import intcurve_series

    Specify a starting point and a vector field:

    >>> start_point = R2_r.point([x, y])
    >>> vector_field = R2_r.e_x

    Calculate the series:

    >>> intcurve_series(vector_field, t, start_point, n=3)
    Matrix([
    [t + x],
    [    y]])

    Or get the elements of the expansion in a list:

    >>> series = intcurve_series(vector_field, t, start_point, n=3, coeffs=True)
    >>> series[0]
    Matrix([
    [x],
    [y]])
    >>> series[1]
    Matrix([
    [t],
    [0]])
    >>> series[2]
    Matrix([
    [0],
    [0]])

    The series in the polar coordinate system:

    >>> series = intcurve_series(vector_field, t, start_point,
    ...             n=3, coord_sys=R2_p, coeffs=True)
    >>> series[0]
    Matrix([
    [sqrt(x**2 + y**2)],
    [      atan2(y, x)]])
    >>> series[1]
    Matrix([
    [t*x/sqrt(x**2 + y**2)],
    [   -t*y/(x**2 + y**2)]])
    >>> series[2]
    Matrix([
    [t**2*(-x**2/(x**2 + y**2)**(3/2) + 1/sqrt(x**2 + y**2))/2],
    [                                t**2*x*y/(x**2 + y**2)**2]])

    See Also
    ========

    intcurve_diffequ

    """
    if contravariant_order(vector_field) != 1 or covariant_order(vector_field):
        raise ValueError('The supplied field was not a vector field.')

    def iter_vfield(scalar_field, i):
        """Return ``vector_field`` called `i` times on ``scalar_field``."""
        return reduce(lambda s, v: v.rcall(s), [vector_field, ]*i, scalar_field)

    def taylor_terms_per_coord(coord_function):
        """Return the series for one of the coordinates."""
        return [param**i*iter_vfield(coord_function, i).rcall(start_point)/factorial(i)
                for i in range(n)]
    coord_sys = coord_sys if coord_sys else start_point._coord_sys
    coord_functions = coord_sys.coord_functions()
    taylor_terms = [taylor_terms_per_coord(f) for f in coord_functions]
    if coeffs:
        return [Matrix(t) for t in zip(*taylor_terms)]
    else:
        return Matrix([sum(c) for c in taylor_terms])


def intcurve_diffequ(vector_field, param, start_point, coord_sys=None):
    r"""Return the differential equation for an integral curve of the field.

    Explanation
    ===========

    Integral curve is a function `\gamma` taking a parameter in `R` to a point
    in the manifold. It verifies the equation:

    `V(f)\big(\gamma(t)\big) = \frac{d}{dt}f\big(\gamma(t)\big)`

    where the given ``vector_field`` is denoted as `V`. This holds for any
    value `t` for the parameter and any scalar field `f`.

    This function returns the differential equation of `\gamma(t)` in terms of the
    coordinate system ``coord_sys``. The equations and expansions are necessarily
    done in coordinate-system-dependent way as there is no other way to
    represent movement between points on the manifold (i.e. there is no such
    thing as a difference of points for a general manifold).

    Parameters
    ==========

    vector_field
        the vector field for which an integral curve will be given

    param
        the argument of the function `\gamma` from R to the curve

    start_point
        the point which corresponds to `\gamma(0)`

    coord_sys
        the coordinate system in which to give the equations

    Returns
    =======

    a tuple of (equations, initial conditions)

    Examples
    ========

    Use the predefined R2 manifold:

    >>> from sympy.abc import t
    >>> from sympy.diffgeom.rn import R2, R2_p, R2_r
    >>> from sympy.diffgeom import intcurve_diffequ

    Specify a starting point and a vector field:

    >>> start_point = R2_r.point([0, 1])
    >>> vector_field = -R2.y*R2.e_x + R2.x*R2.e_y

    Get the equation:

    >>> equations, init_cond = intcurve_diffequ(vector_field, t, start_point)
    >>> equations
    [f_1(t) + Derivative(f_0(t), t), -f_0(t) + Derivative(f_1(t), t)]
    >>> init_cond
    [f_0(0), f_1(0) - 1]

    The series in the polar coordinate system:

    >>> equations, init_cond = intcurve_diffequ(vector_field, t, start_point, R2_p)
    >>> equations
    [Derivative(f_0(t), t), Derivative(f_1(t), t) - 1]
    >>> init_cond
    [f_0(0) - 1, f_1(0) - pi/2]

    See Also
    ========

    intcurve_series

    """
    if contravariant_order(vector_field) != 1 or covariant_order(vector_field):
        raise ValueError('The supplied field was not a vector field.')
    coord_sys = coord_sys if coord_sys else start_point._coord_sys
    gammas = [Function('f_%d' % i)(param) for i in range(
        start_point._coord_sys.dim)]
    arbitrary_p = Point(coord_sys, gammas)
    coord_functions = coord_sys.coord_functions()
    equations = [simplify(diff(cf.rcall(arbitrary_p), param) - vector_field.rcall(cf).rcall(arbitrary_p))
                 for cf in coord_functions]
    init_cond = [simplify(cf.rcall(arbitrary_p).subs(param, 0) - cf.rcall(start_point))
                 for cf in coord_functions]
    return equations, init_cond


###############################################################################
# Helpers
###############################################################################
def dummyfy(args, exprs):
    # TODO Is this a good idea?
    d_args = Matrix([s.as_dummy() for s in args])
    reps = dict(zip(args, d_args))
    d_exprs = Matrix([_sympify(expr).subs(reps) for expr in exprs])
    return d_args, d_exprs


###############################################################################
# Helpers
###############################################################################
def contravariant_order(expr, _strict=False):
    """Return the contravariant order of an expression.

    Examples
    ========

    >>> from sympy.diffgeom import contravariant_order
    >>> from sympy.diffgeom.rn import R2
    >>> from sympy.abc import a

    >>> contravariant_order(a)
    0
    >>> contravariant_order(a*R2.x + 2)
    0
    >>> contravariant_order(a*R2.x*R2.e_y + R2.e_x)
    1

    """
    # TODO move some of this to class methods.
    # TODO rewrite using the .as_blah_blah methods
    if isinstance(expr, Add):
        orders = [contravariant_order(e) for e in expr.args]
        if len(set(orders)) != 1:
            raise ValueError('Misformed expression containing contravariant fields of varying order.')
        return orders[0]
    elif isinstance(expr, Mul):
        orders = [contravariant_order(e) for e in expr.args]
        not_zero = [o for o in orders if o != 0]
        if len(not_zero) > 1:
            raise ValueError('Misformed expression containing multiplication between vectors.')
        return 0 if not not_zero else not_zero[0]
    elif isinstance(expr, Pow):
        if covariant_order(expr.base) or covariant_order(expr.exp):
            raise ValueError(
                'Misformed expression containing a power of a vector.')
        return 0
    elif isinstance(expr, BaseVectorField):
        return 1
    elif isinstance(expr, TensorProduct):
        return sum(contravariant_order(a) for a in expr.args)
    elif not _strict or expr.atoms(BaseScalarField):
        return 0
    else:  # If it does not contain anything related to the diffgeom module and it is _strict
        return -1


def covariant_order(expr, _strict=False):
    """Return the covariant order of an expression.

    Examples
    ========

    >>> from sympy.diffgeom import covariant_order
    >>> from sympy.diffgeom.rn import R2
    >>> from sympy.abc import a

    >>> covariant_order(a)
    0
    >>> covariant_order(a*R2.x + 2)
    0
    >>> covariant_order(a*R2.x*R2.dy + R2.dx)
    1

    """
    # TODO move some of this to class methods.
    # TODO rewrite using the .as_blah_blah methods
    if isinstance(expr, Add):
        orders = [covariant_order(e) for e in expr.args]
        if len(set(orders)) != 1:
            raise ValueError('Misformed expression containing form fields of varying order.')
        return orders[0]
    elif isinstance(expr, Mul):
        orders = [covariant_order(e) for e in expr.args]
        not_zero = [o for o in orders if o != 0]
        if len(not_zero) > 1:
            raise ValueError('Misformed expression containing multiplication between forms.')
        return 0 if not not_zero else not_zero[0]
    elif isinstance(expr, Pow):
        if covariant_order(expr.base) or covariant_order(expr.exp):
            raise ValueError(
                'Misformed expression containing a power of a form.')
        return 0
    elif isinstance(expr, Differential):
        return covariant_order(*expr.args) + 1
    elif isinstance(expr, TensorProduct):
        return sum(covariant_order(a) for a in expr.args)
    elif not _strict or expr.atoms(BaseScalarField):
        return 0
    else:  # If it does not contain anything related to the diffgeom module and it is _strict
        return -1


###############################################################################
# Coordinate transformation functions
###############################################################################
def vectors_in_basis(expr, to_sys):
    """Transform all base vectors in base vectors of a specified coord basis.
    While the new base vectors are in the new coordinate system basis, any
    coefficients are kept in the old system.

    Examples
    ========

    >>> from sympy.diffgeom import vectors_in_basis
    >>> from sympy.diffgeom.rn import R2_r, R2_p

    >>> vectors_in_basis(R2_r.e_x, R2_p)
    -y*e_theta/(x**2 + y**2) + x*e_rho/sqrt(x**2 + y**2)
    >>> vectors_in_basis(R2_p.e_r, R2_r)
    sin(theta)*e_y + cos(theta)*e_x

    """
    vectors = list(expr.atoms(BaseVectorField))
    new_vectors = []
    for v in vectors:
        cs = v._coord_sys
        jac = cs.jacobian(to_sys, cs.coord_functions())
        new = (jac.T*Matrix(to_sys.base_vectors()))[v._index]
        new_vectors.append(new)
    return expr.subs(list(zip(vectors, new_vectors)))


###############################################################################
# Coordinate-dependent functions
###############################################################################
def twoform_to_matrix(expr):
    """Return the matrix representing the twoform.

    For the twoform `w` return the matrix `M` such that `M[i,j]=w(e_i, e_j)`,
    where `e_i` is the i-th base vector field for the coordinate system in
    which the expression of `w` is given.

    Examples
    ========

    >>> from sympy.diffgeom.rn import R2
    >>> from sympy.diffgeom import twoform_to_matrix, TensorProduct
    >>> TP = TensorProduct

    >>> twoform_to_matrix(TP(R2.dx, R2.dx) + TP(R2.dy, R2.dy))
    Matrix([
    [1, 0],
    [0, 1]])
    >>> twoform_to_matrix(R2.x*TP(R2.dx, R2.dx) + TP(R2.dy, R2.dy))
    Matrix([
    [x, 0],
    [0, 1]])
    >>> twoform_to_matrix(TP(R2.dx, R2.dx) + TP(R2.dy, R2.dy) - TP(R2.dx, R2.dy)/2)
    Matrix([
    [   1, 0],
    [-1/2, 1]])

    """
    if covariant_order(expr) != 2 or contravariant_order(expr):
        raise ValueError('The input expression is not a two-form.')
    coord_sys = _find_coords(expr)
    if len(coord_sys) != 1:
        raise ValueError('The input expression concerns more than one '
                         'coordinate systems, hence there is no unambiguous '
                         'way to choose a coordinate system for the matrix.')
    coord_sys = coord_sys.pop()
    vectors = coord_sys.base_vectors()
    expr = expr.expand()
    matrix_content = [[expr.rcall(v1, v2) for v1 in vectors]
                      for v2 in vectors]
    return Matrix(matrix_content)


def metric_to_Christoffel_1st(expr):
    """Return the nested list of Christoffel symbols for the given metric.
    This returns the Christoffel symbol of first kind that represents the
    Levi-Civita connection for the given metric.

    Examples
    ========

    >>> from sympy.diffgeom.rn import R2
    >>> from sympy.diffgeom import metric_to_Christoffel_1st, TensorProduct
    >>> TP = TensorProduct

    >>> metric_to_Christoffel_1st(TP(R2.dx, R2.dx) + TP(R2.dy, R2.dy))
    [[[0, 0], [0, 0]], [[0, 0], [0, 0]]]
    >>> metric_to_Christoffel_1st(R2.x*TP(R2.dx, R2.dx) + TP(R2.dy, R2.dy))
    [[[1/2, 0], [0, 0]], [[0, 0], [0, 0]]]

    """
    matrix = twoform_to_matrix(expr)
    if not matrix.is_symmetric():
        raise ValueError(
            'The two-form representing the metric is not symmetric.')
    coord_sys = _find_coords(expr).pop()
    deriv_matrices = [matrix.applyfunc(lambda a: d(a))
                      for d in coord_sys.base_vectors()]
    indices = list(range(coord_sys.dim))
    christoffel = [[[(deriv_matrices[k][i, j] + deriv_matrices[j][i, k] - deriv_matrices[i][j, k])/2
                     for k in indices]
                    for j in indices]
                   for i in indices]
    return ImmutableDenseNDimArray(christoffel)


def metric_to_Christoffel_2nd(expr):
    """Return the nested list of Christoffel symbols for the given metric.
    This returns the Christoffel symbol of second kind that represents the
    Levi-Civita connection for the given metric.

    Examples
    ========

    >>> from sympy.diffgeom.rn import R2
    >>> from sympy.diffgeom import metric_to_Christoffel_2nd, TensorProduct
    >>> TP = TensorProduct

    >>> metric_to_Christoffel_2nd(TP(R2.dx, R2.dx) + TP(R2.dy, R2.dy))
    [[[0, 0], [0, 0]], [[0, 0], [0, 0]]]
    >>> metric_to_Christoffel_2nd(R2.x*TP(R2.dx, R2.dx) + TP(R2.dy, R2.dy))
    [[[1/(2*x), 0], [0, 0]], [[0, 0], [0, 0]]]

    """
    ch_1st = metric_to_Christoffel_1st(expr)
    coord_sys = _find_coords(expr).pop()
    indices = list(range(coord_sys.dim))
    # XXX workaround, inverting a matrix does not work if it contains non
    # symbols
    #matrix = twoform_to_matrix(expr).inv()
    matrix = twoform_to_matrix(expr)
    s_fields = set()
    for e in matrix:
        s_fields.update(e.atoms(BaseScalarField))
    s_fields = list(s_fields)
    dums = coord_sys.symbols
    matrix = matrix.subs(list(zip(s_fields, dums))).inv().subs(list(zip(dums, s_fields)))
    # XXX end of workaround
    christoffel = [[[Add(*[matrix[i, l]*ch_1st[l, j, k] for l in indices])
                     for k in indices]
                    for j in indices]
                   for i in indices]
    return ImmutableDenseNDimArray(christoffel)


def metric_to_Riemann_components(expr):
    """Return the components of the Riemann tensor expressed in a given basis.

    Given a metric it calculates the components of the Riemann tensor in the
    canonical basis of the coordinate system in which the metric expression is
    given.

    Examples
    ========

    >>> from sympy import exp
    >>> from sympy.diffgeom.rn import R2
    >>> from sympy.diffgeom import metric_to_Riemann_components, TensorProduct
    >>> TP = TensorProduct

    >>> metric_to_Riemann_components(TP(R2.dx, R2.dx) + TP(R2.dy, R2.dy))
    [[[[0, 0], [0, 0]], [[0, 0], [0, 0]]], [[[0, 0], [0, 0]], [[0, 0], [0, 0]]]]
    >>> non_trivial_metric = exp(2*R2.r)*TP(R2.dr, R2.dr) + \
        R2.r**2*TP(R2.dtheta, R2.dtheta)
    >>> non_trivial_metric
    exp(2*rho)*TensorProduct(drho, drho) + rho**2*TensorProduct(dtheta, dtheta)
    >>> riemann = metric_to_Riemann_components(non_trivial_metric)
    >>> riemann[0, :, :, :]
    [[[0, 0], [0, 0]], [[0, exp(-2*rho)*rho], [-exp(-2*rho)*rho, 0]]]
    >>> riemann[1, :, :, :]
    [[[0, -1/rho], [1/rho, 0]], [[0, 0], [0, 0]]]

    """
    ch_2nd = metric_to_Christoffel_2nd(expr)
    coord_sys = _find_coords(expr).pop()
    indices = list(range(coord_sys.dim))
    deriv_ch = [[[[d(ch_2nd[i, j, k])
                   for d in coord_sys.base_vectors()]
                  for k in indices]
                 for j in indices]
                for i in indices]
    riemann_a = [[[[deriv_ch[rho][sig][nu][mu] - deriv_ch[rho][sig][mu][nu]
                    for nu in indices]
                   for mu in indices]
                  for sig in indices]
                     for rho in indices]
    riemann_b = [[[[Add(*[ch_2nd[rho, l, mu]*ch_2nd[l, sig, nu] - ch_2nd[rho, l, nu]*ch_2nd[l, sig, mu] for l in indices])
                    for nu in indices]
                   for mu in indices]
                  for sig in indices]
                 for rho in indices]
    riemann = [[[[riemann_a[rho][sig][mu][nu] + riemann_b[rho][sig][mu][nu]
                  for nu in indices]
                     for mu in indices]
                for sig in indices]
               for rho in indices]
    return ImmutableDenseNDimArray(riemann)


def metric_to_Ricci_components(expr):

    """Return the components of the Ricci tensor expressed in a given basis.

    Given a metric it calculates the components of the Ricci tensor in the
    canonical basis of the coordinate system in which the metric expression is
    given.

    Examples
    ========

    >>> from sympy import exp
    >>> from sympy.diffgeom.rn import R2
    >>> from sympy.diffgeom import metric_to_Ricci_components, TensorProduct
    >>> TP = TensorProduct

    >>> metric_to_Ricci_components(TP(R2.dx, R2.dx) + TP(R2.dy, R2.dy))
    [[0, 0], [0, 0]]
    >>> non_trivial_metric = exp(2*R2.r)*TP(R2.dr, R2.dr) + \
                             R2.r**2*TP(R2.dtheta, R2.dtheta)
    >>> non_trivial_metric
    exp(2*rho)*TensorProduct(drho, drho) + rho**2*TensorProduct(dtheta, dtheta)
    >>> metric_to_Ricci_components(non_trivial_metric)
    [[1/rho, 0], [0, exp(-2*rho)*rho]]

    """
    riemann = metric_to_Riemann_components(expr)
    coord_sys = _find_coords(expr).pop()
    indices = list(range(coord_sys.dim))
    ricci = [[Add(*[riemann[k, i, k, j] for k in indices])
              for j in indices]
             for i in indices]
    return ImmutableDenseNDimArray(ricci)

###############################################################################
# Classes for deprecation
###############################################################################

class _deprecated_container:
    # This class gives deprecation warning.
    # When deprecated features are completely deleted, this should be removed as well.
    # See https://github.com/sympy/sympy/pull/19368
    def __init__(self, feature, useinstead, issue, version, data):
        super().__init__(data)
        self.feature = feature
        self.useinstead = useinstead
        self.issue = issue
        self.version = version

    def warn(self):
        SymPyDeprecationWarning(
                    feature=self.feature,
                    useinstead=self.useinstead,
                    issue=self.issue,
                    deprecated_since_version=self.version).warn()

    def __iter__(self):
        self.warn()
        return super().__iter__()

    def __getitem__(self, key):
        self.warn()
        return super().__getitem__(key)

    def __contains__(self, key):
        self.warn()
        return super().__contains__(key)

class _deprecated_list(_deprecated_container, list):
    pass

class _deprecated_dict(_deprecated_container, dict):
    pass
