# Copyright (C) 2004, Thomas Hamelryck (thamelry@binf.ku.dk)
# This code is part of the Biopython distribution and governed by its
# license.  Please see the LICENSE file that should have been included
# as part of this package.

"""Vector class, including rotation-related functions."""

import numpy


def m2rotaxis(m):
    """
    Return angles, axis pair that corresponds to rotation matrix m.
    """
    # Angle always between 0 and pi
    # Sense of rotation is defined by axis orientation
    t=0.5*(numpy.trace(m)-1)
    t=max(-1, t)
    t=min(1, t)
    angle=numpy.arccos(t)
    if angle<1e-15:
        # Angle is 0
        return 0.0, Vector(1,0,0)
    elif angle<numpy.pi:
        # Angle is smaller than pi
        x=m[2,1]-m[1,2]
        y=m[0,2]-m[2,0]
        z=m[1,0]-m[0,1]
        axis=Vector(x,y,z)
        axis.normalize()
        return angle, axis
    else:
        # Angle is pi - special case!
        m00=m[0,0]
        m11=m[1,1]
        m22=m[2,2]
        if m00>m11 and m00>m22:
            x=numpy.sqrt(m00-m11-m22+0.5)
            y=m[0,1]/(2*x)
            z=m[0,2]/(2*x)
        elif m11>m00 and m11>m22:
            y=numpy.sqrt(m11-m00-m22+0.5)
            x=m[0,1]/(2*y)
            z=m[1,2]/(2*y)
        else:
            z=numpy.sqrt(m22-m00-m11+0.5)
            x=m[0,2]/(2*z)
            y=m[1,2]/(2*z)
        axis=Vector(x,y,z)
        axis.normalize()
        return numpy.pi, axis


def vector_to_axis(line, point):
    """
    Returns the vector between a point and
    the closest point on a line (ie. the perpendicular
    projection of the point on the line).

    @type line: L{Vector}
    @param line: vector defining a line

    @type point: L{Vector}
    @param point: vector defining the point
    """
    line=line.normalized()
    np=point.norm()
    angle=line.angle(point)
    return point-line**(np*numpy.cos(angle))


def rotaxis2m(theta, vector):
    """
    Calculate a left multiplying rotation matrix that rotates
    theta rad around vector.

    Example: 
    
        >>> m=rotaxis(pi, Vector(1,0,0))
        >>> rotated_vector=any_vector.left_multiply(m)

    @type theta: float
    @param theta: the rotation angle


    @type vector: L{Vector}
    @param vector: the rotation axis

    @return: The rotation matrix, a 3x3 Numeric array.
    """
    vector=vector.copy()
    vector.normalize()
    c=numpy.cos(theta)
    s=numpy.sin(theta)
    t=1-c
    x,y,z=vector.get_array()
    rot=numpy.zeros((3,3))
    # 1st row
    rot[0,0]=t*x*x+c
    rot[0,1]=t*x*y-s*z
    rot[0,2]=t*x*z+s*y
    # 2nd row
    rot[1,0]=t*x*y+s*z
    rot[1,1]=t*y*y+c
    rot[1,2]=t*y*z-s*x
    # 3rd row
    rot[2,0]=t*x*z-s*y
    rot[2,1]=t*y*z+s*x
    rot[2,2]=t*z*z+c
    return rot

rotaxis=rotaxis2m

def refmat(p,q):
    """
    Return a (left multiplying) matrix that mirrors p onto q.

    Example:
        >>> mirror=refmat(p,q)
        >>> qq=p.left_multiply(mirror)
        >>> print q, qq # q and qq should be the same

    @type p,q: L{Vector}
    @return: The mirror operation, a 3x3 Numeric array. 
    """
    p.normalize()
    q.normalize()
    if (p-q).norm()<1e-5:
        return numpy.identity(3)
    pq=p-q
    pq.normalize()
    b=pq.get_array()
    b.shape=(3, 1)
    i=numpy.identity(3)
    ref=i-2*numpy.dot(b, numpy.transpose(b))
    return ref

def rotmat(p,q):
    """
    Return a (left multiplying) matrix that rotates p onto q.

    Example:
        >>> r=rotmat(p,q)
        >>> print q, p.left_multiply(r)

    @param p: moving vector
    @type p: L{Vector}

    @param q: fixed vector
    @type q: L{Vector}

    @return: rotation matrix that rotates p onto q
    @rtype: 3x3 Numeric array
    """
    rot=numpy.dot(refmat(q, -p), refmat(p, -p))
    return rot

def calc_angle(v1, v2, v3):
    """
    Calculate the angle between 3 vectors
    representing 3 connected points.

    @param v1, v2, v3: the tree points that define the angle
    @type v1, v2, v3: L{Vector}
    
    @return: angle
    @rtype: float
    """
    v1=v1-v2
    v3=v3-v2
    return v1.angle(v3)

def calc_dihedral(v1, v2, v3, v4):
    """
    Calculate the dihedral angle between 4 vectors
    representing 4 connected points. The angle is in
    ]-pi, pi].

    @param v1, v2, v3, v4: the four points that define the dihedral angle
    @type v1, v2, v3, v4: L{Vector}
    """
    ab=v1-v2
    cb=v3-v2
    db=v4-v3
    u=ab**cb
    v=db**cb
    w=u**v
    angle=u.angle(v)
    # Determine sign of angle
    try:
        if cb.angle(w)>0.001:
            angle=-angle
    except ZeroDivisionError:
        # dihedral=pi
        pass
    return angle

class Vector(object):
    "3D vector"

    def __init__(self, x, y=None, z=None):
        if y is None and z is None:
            # Array, list, tuple...
            if len(x)!=3:
                raise ValueError("Vector: x is not a "
                                 "list/tuple/array of 3 numbers")
            self._ar=numpy.array(x, 'd')
        else:
            # Three numbers
            self._ar=numpy.array((x, y, z), 'd')

    def __repr__(self):
        x,y,z=self._ar
        return "<Vector %.2f, %.2f, %.2f>" % (x,y,z)

    def __neg__(self):
        "Return Vector(-x, -y, -z)"
        a=-self._ar
        return Vector(a)

    def __add__(self, other):
        "Return Vector+other Vector or scalar"
        if isinstance(other, Vector):
            a=self._ar+other._ar
        else:
            a=self._ar+numpy.array(other)
        return Vector(a)

    def __sub__(self, other):
        "Return Vector-other Vector or scalar"
        if isinstance(other, Vector):
            a=self._ar-other._ar
        else:
            a=self._ar-numpy.array(other)
        return Vector(a)

    def __mul__(self, other):
        "Return Vector.Vector (dot product)"
        return sum(self._ar*other._ar)

    def __div__(self, x):
        "Return Vector(coords/a)"
        a=self._ar/numpy.array(x)
        return Vector(a)

    def __pow__(self, other):
        "Return VectorxVector (cross product) or Vectorxscalar"
        if isinstance(other, Vector):
            a,b,c=self._ar
            d,e,f=other._ar
            c1=numpy.linalg.det(numpy.array(((b,c), (e,f))))
            c2=-numpy.linalg.det(numpy.array(((a,c), (d,f))))
            c3=numpy.linalg.det(numpy.array(((a,b), (d,e))))
            return Vector(c1,c2,c3)
        else:
            a=self._ar*numpy.array(other)
            return Vector(a)

    def __getitem__(self, i):
        return self._ar[i]

    def __setitem__(self, i, value):
        self._ar[i]=value

    def norm(self):
        "Return vector norm"
        return numpy.sqrt(sum(self._ar*self._ar))

    def normsq(self):
        "Return square of vector norm"
        return abs(sum(self._ar*self._ar))

    def normalize(self):
        "Normalize the Vector"
        self._ar=self._ar/self.norm()

    def normalized(self):
        "Return a normalized copy of the Vector"
        v=self.copy()
        v.normalize()
        return v

    def angle(self, other):
        "Return angle between two vectors"
        n1=self.norm()
        n2=other.norm()
        c=(self*other)/(n1*n2)
        # Take care of roundoff errors
        c=min(c,1)
        c=max(-1,c)
        return numpy.arccos(c)

    def get_array(self):
        "Return (a copy of) the array of coordinates"
        return numpy.array(self._ar)

    def left_multiply(self, matrix):
        "Return Vector=Matrix x Vector"
        a=numpy.dot(matrix, self._ar)
        return Vector(a)

    def right_multiply(self, matrix):
        "Return Vector=Vector x Matrix"
        a=numpy.dot(self._ar, matrix)
        return Vector(a)

    def copy(self):
        "Return a deep copy of the Vector"
        return Vector(self._ar)

if __name__=="__main__":

        from numpy.random import random

        v1=Vector(0,0,1)
        v2=Vector(0,0,0)
        v3=Vector(0,1,0)
        v4=Vector(1,1,0)

        v4.normalize()

        print v4

        print calc_angle(v1, v2, v3)
        dih=calc_dihedral(v1, v2, v3, v4)
        # Test dihedral sign
        assert(dih>0)
        print "DIHEDRAL ", dih

        ref=refmat(v1, v3)
        rot=rotmat(v1, v3)

        print v3
        print v1.left_multiply(ref)
        print v1.left_multiply(rot)
        print v1.right_multiply(numpy.transpose(rot))

        # -
        print v1-v2
        print v1-1
        print v1+(1,2,3)
        # +
        print v1+v2
        print v1+3
        print v1-(1,2,3)
        # *
        print v1*v2
        # /
        print v1/2
        print v1/(1,2,3)
        # **
        print v1**v2
        print v1**2
        print v1**(1,2,3)
        # norm
        print v1.norm()
        # norm squared
        print v1.normsq()
        # setitem
        v1[2]=10
        print v1
        # getitem
        print v1[2]

        print numpy.array(v1)

        print "ROT"

        angle=random()*numpy.pi
        axis=Vector(random(3)-random(3))
        axis.normalize()

        m=rotaxis(angle, axis)

        cangle, caxis=m2rotaxis(m)

        print angle-cangle
        print axis-caxis
        print
