##  Copyright 1997-1999 by Konrad Hinsen, except as noted below.

##  Permission to use, copy, modify, and distribute this software and its
##  documentation for any purpose and without fee is hereby granted,
##  provided that the above copyright notice appear in all copies and that
##  both that copyright notice and this permission notice appear in
##  supporting documentation.

##  THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE,
##  INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO
##  EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR
##  CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF
##  USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
##  OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
##  PERFORMANCE OF THIS SOFTWARE.

# This module defines 3d geometrical vectors with the standard
# operations on them. The elements are stored in an
# array.
#
# Written by: Konrad Hinsen <hinsen@cnrs-orleans.fr>
# Last revision: 1999-7-23
#

_undocumented = 1

import numpy
import types
import TensorModule


class Vector:

    """Vector in 3D space

    Constructor:

    - Vector(|x|, |y|, |z|)   (from three coordinates)
    - Vector(|coordinates|)   (from any sequence containing three coordinates)

    Vectors support the usual arithmetic operations
    ('v1', 'v2': vectors, 's': scalar): 

    -  'v1+v2'           (addition)
    -  'v1-v2'           (subtraction)
    -  'v1*v2'           (scalar product)
    -  's*v1', 'v1*s'    (multiplication with a scalar)
    -  'v1/s'            (division by a scalar)

    The three coordinates can be extracted by indexing.

    Vectors are *immutable*, i.e. their elements cannot be changed.

    Vector elements can be any objects on which the standard
    arithmetic operations plus the functions sqrt and arccos are defined.
    """

    is_vector = 1

    def __init__(self, x=None, y=None, z=None):
	if x is None:
	    self.array = [0.,0.,0.]
	elif y is None and z is None:
	    self.array = x
	else:
	    self.array = [x,y,z]
	self.array = numpy.array(self.array)

    def __getstate__(self):
	return list(self.array)

    def __setstate__(self, state):
	self.array = numpy.array(state)

    def __copy__(self, memo = None):
	return self
    __deepcopy__ = __copy__

    def __repr__(self):
	return 'Vector(%s,%s,%s)' % (`self.array[0]`,\
				     `self.array[1]`,`self.array[2]`)

    def __str__(self):
	return `list(self.array)`

    def __add__(self, other):
	return Vector(self.array+other.array)
    __radd__ = __add__

    def __neg__(self):
	return Vector(-self.array)

    def __sub__(self, other):
	return Vector(self.array-other.array)

    def __rsub__(self, other):
	return Vector(other.array-self.array)

    def __mul__(self, other):
	if isVector(other):
	    return numpy.add.reduce(self.array*other.array)
	elif TensorModule.isTensor(other):
	    product = TensorModule.Tensor(self.array).dot(other)
	    if product.rank == 1:
		return Vector(product.array)
	    else:
		return product
        elif hasattr(other, "_product_with_vector"):
            return other._product_with_vector(self)
	else:
	    return Vector(numpy.multiply(self.array, other))

    def __rmul__(self, other):
	if TensorModule.isTensor(other):
	    product = other.dot(TensorModule.Tensor(self.array))
	    if product.rank == 1:
		return Vector(product.array)
	    else:
		return product
	else:
	    return Vector(numpy.multiply(self.array, other))

    def __div__(self, other):
	if isVector(other):
	    raise TypeError, "Can't divide by a vector"
	else:
	    return Vector(numpy.divide(self.array,1.*other))
	    
    def __rdiv__(self, other):
        raise TypeError, "Can't divide by a vector"

    def __cmp__(self, other):
	return cmp(numpy.add.reduce(abs(self.array-other.array)), 0)

    def __len__(self):
	return 3

    def __getitem__(self, index):
	return self.array[index]

    def x(self):
        "Returns the x coordinate."
	return self.array[0]
    def y(self):
        "Returns the y coordinate."
	return self.array[1]
    def z(self):
        "Returns the z coordinate."
	return self.array[2]

    def length(self):
        "Returns the length (norm)."
	return numpy.sqrt(numpy.add.reduce(self.array*self.array))

    def normal(self):
        "Returns a normalized copy."
	len = numpy.sqrt(numpy.add.reduce(self.array*self.array))
	if len == 0:
	    raise ZeroDivisionError, "Can't normalize a zero-length vector"
	return Vector(numpy.divide(self.array, len))

    def cross(self, other):
        "Returns the cross product with vector |other|."
	if not isVector(other):
	    raise TypeError, "Cross product with non-vector"
	return Vector(self.array[1]*other.array[2]
                                -self.array[2]*other.array[1],
		      self.array[2]*other.array[0]
                                -self.array[0]*other.array[2],
		      self.array[0]*other.array[1]
                                -self.array[1]*other.array[0])

    def asTensor(self):
        "Returns an equivalent tensor object of rank 1."
	return TensorModule.Tensor(self.array, 1)

    def dyadicProduct(self, other):
        "Returns the dyadic product with vector or tensor |other|."
	if isVector(other):
	    return TensorModule.Tensor(self.array, 1) * \
                   TensorModule.Tensor(other.array, 1)
	elif TensorModule.isTensor(other):
	    return TensorModule.Tensor(self.array, 1)*other
	else:
	    raise TypeError, "Dyadic product with non-vector"
	
    def angle(self, other):
        "Returns the angle to vector |other|."
	if not isVector(other):
	    raise TypeError, "Angle between vector and non-vector"
	cosa = numpy.add.reduce(self.array*other.array) / \
	       numpy.sqrt(numpy.add.reduce(self.array*self.array) * \
                            numpy.add.reduce(other.array*other.array))
	cosa = max(-1.,min(1.,cosa))
	return numpy.arccos(cosa)


# Type check

def isVector(x):
    "Return 1 if |x| is a vector."
    return hasattr(x,'is_vector')


# Some useful constants

ex = Vector(1,0,0)
ey = Vector(0,1,0)
ez = Vector(0,0,1)
null = Vector(0.,0.,0.)
