# Automatic first-order derivatives
#
# Written by Konrad Hinsen <hinsen@cnrs-orleans.fr>
# last revision: 2002-6-13
#

"""This module provides automatic differentiation for functions with
any number of variables. Instances of the class DerivVar represent the
values of a function and its partial derivatives with respect to a
list of variables. All common mathematical operations and functions
are available for these numbers.  There is no restriction on the type
of the numbers fed into the code; it works for real and complex
numbers as well as for any Python type that implements the necessary
operations.

This module is as far as possible compatible with the n-th order
derivatives module Derivatives. If only first-order derivatives
are required, this module is faster than the general one.

Example:

  >>>print sin(DerivVar(2))

  produces the output

  >>>(0.909297426826, [-0.416146836547])

The first number is the value of sin(2); the number in the following
list is the value of the derivative of sin(x) at x=2, i.e. cos(2).

When there is more than one variable, DerivVar must be called with
an integer second argument that specifies the number of the variable.

Example:

  >>>x = DerivVar(7., 0)
  >>>y = DerivVar(42., 1)
  >>>z = DerivVar(pi, 2)
  >>>print (sqrt(pow(x,2)+pow(y,2)+pow(z,2)))

  produces the output

  >>>(42.6950770511, [0.163953328662, 0.98371997197, 0.0735820818365])

The numbers in the list are the partial derivatives with respect
to x, y, and z, respectively.

Note: It doesn't make sense to use DerivVar with different values
for the same variable index in one calculation, but there is
no check for this. I.e.

  >>>print DerivVar(3, 0)+DerivVar(5, 0)

  produces

  >>>(8, [2])

but this result is meaningless.
"""


import Numeric


# The following class represents variables with derivatives:

class DerivVar:

    """Variable with derivatives

    Constructor: DerivVar(|value|, |index| = 0)

    Arguments:

    |value| -- the numerical value of the variable

    |index| -- the variable index (an integer), which serves to
               distinguish between variables and as an index for
               the derivative lists. Each explicitly created
               instance of DerivVar must have a unique index.

    Indexing with an integer yields the derivatives of the corresponding
    order.
    """

    def __init__(self, value, index=0, order=1):
	if order > 1:
	    raise ValueError, 'Only first-order derivatives'
	self.value = value
	if order == 0:
	    self.deriv = []
	elif type(index) == type([]):
	    self.deriv = index
	else:
	    self.deriv = index*[0] + [1]

    def __getitem__(self, item):
	if item < 0 or item > 1:
	    raise ValueError, 'Index out of range'
	if item == 0:
	    return self.value
	else:
	    return self.deriv

    def __repr__(self):
	return `(self.value, self.deriv)`

    def __str__(self):
	return str((self.value, self.deriv))

    def __coerce__(self, other):
	if isDerivVar(other):
	    return self, other
	else:
	    return self, DerivVar(other, [])

    def __cmp__(self, other):
	return cmp(self.value, other.value)

    def __neg__(self):
	return DerivVar(-self.value,map(lambda a: -a, self.deriv))

    def __pos__(self):
	return self

    def __abs__(self): # cf maple signum # derivate of abs
        absvalue = abs(self.value)
	return DerivVar(absvalue, map(lambda a, d=self.value/absvalue:
                                      d*a, self.deriv))
    def __nonzero__(self):
	return self.value != 0

    def __add__(self, other):
	return DerivVar(self.value + other.value,
			_mapderiv(lambda a,b: a+b, self.deriv, other.deriv))
    __radd__ = __add__

    def __sub__(self, other):
	return DerivVar(self.value - other.value,
			_mapderiv(lambda a,b: a-b, self.deriv, other.deriv))

    def __rsub__(self, other):
	return DerivVar(other.value - self.value,
			_mapderiv(lambda a,b: a-b, other.deriv, self.deriv))

    def __mul__(self, other):
	return DerivVar(self.value*other.value,
			_mapderiv(lambda a,b: a+b,
				  map(lambda x,f=other.value:f*x, self.deriv),
				  map(lambda x,f=self.value:f*x, other.deriv)))
    __rmul__ = __mul__

    def __div__(self, other):
	if not other.value:
	    raise ZeroDivisionError, 'DerivVar division'
	inv = 1./other.value
	return DerivVar(self.value*inv,
			_mapderiv(lambda a,b: a-b,
				  map(lambda x,f=inv: f*x, self.deriv),
				  map(lambda x,f=self.value*inv*inv: f*x,
				      other.deriv)))
    def __rdiv__(self, other):
	return other/self

    def __pow__(self, other, z=None):
	if z is not None:
	    raise TypeError, 'DerivVar does not support ternary pow()'
	val1 = pow(self.value, other.value-1)
	val = val1*self.value
	deriv1 = map(lambda x,f=val1*other.value: f*x, self.deriv)
	if isDerivVar(other) and len(other.deriv) > 0:
	    deriv2 = map(lambda x, f=val*Numeric.log(self.value): f*x,
                         other.deriv)
	    return DerivVar(val,_mapderiv(lambda a,b: a+b, deriv1, deriv2))
	else:
	    return DerivVar(val,deriv1)

    def __rpow__(self, other):
	return pow(other, self)

    def exp(self):
	v = Numeric.exp(self.value)
	return DerivVar(v, map(lambda x,f=v: f*x, self.deriv))

    def log(self):
	v = Numeric.log(self.value)
	d = 1./self.value
	return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def log10(self):
        v = Numeric.log10(self.value)
        d = 1./(self.value * Numeric.log(10))
        return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def sqrt(self):
	v = Numeric.sqrt(self.value)
	d = 0.5/v
	return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def sign(self):
        if self.value == 0:
	    raise ValueError, "can't differentiate sign() at zero"
	return DerivVar(Numeric.sign(self.value), 0)

    def sin(self):
	v = Numeric.sin(self.value)
	d = Numeric.cos(self.value)
	return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def cos(self):
	v = Numeric.cos(self.value)
	d = -Numeric.sin(self.value)
	return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def tan(self):
	v = Numeric.tan(self.value)
	d = 1.+pow(v,2)
	return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def sinh(self):
	v = Numeric.sinh(self.value)
	d = Numeric.cosh(self.value)
	return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def cosh(self):
	v = Numeric.cosh(self.value)
	d = Numeric.sinh(self.value)
	return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def tanh(self):
	v = Numeric.tanh(self.value)
	d = 1./pow(Numeric.cosh(self.value),2)
	return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def arcsin(self):
	v = Numeric.arcsin(self.value)
	d = 1./Numeric.sqrt(1.-pow(self.value,2))
	return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def arccos(self):
	v = Numeric.arccos(self.value)
	d = -1./Numeric.sqrt(1.-pow(self.value,2))
	return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def arctan(self):
	v = Numeric.arctan(self.value)
	d = 1./(1.+pow(self.value,2))
	return DerivVar(v, map(lambda x,f=d: f*x, self.deriv))

    def arctan2(self, other):
        den = self.value*self.value+other.value*other.value
        s = self.value/den
        o = other.value/den
	return DerivVar(Numeric.arctan2(self.value, other.value),
			_mapderiv(lambda a,b: a-b,
				  map(lambda x,f=o: f*x, self.deriv),
				  map(lambda x,f=s: f*x, other.deriv)))

# Type check

def isDerivVar(x):
    "Returns 1 if |x| is a DerivVar object."
    return hasattr(x,'value') and hasattr(x,'deriv')

# Map a binary function on two first derivative lists

def _mapderiv(func, a, b):
    nvars = max(len(a), len(b))
    a = a + (nvars-len(a))*[0]
    b = b + (nvars-len(b))*[0]
    return map(func, a, b)


# Define vector of DerivVars

def DerivVector(x, y, z, index=0):

    """Returns a vector whose components are DerivVar objects.

    Arguments:

    |x|, |y|, |z| -- vector components (numbers)

    |index| -- the DerivVar index for the x component. The y and z
               components receive consecutive indices.
    """

    from Scientific.Geometry.Vector import Vector
    if isDerivVar(x) and isDerivVar(y) and isDerivVar(z):
	return Vector(x, y, z)
    else:
	return Vector(DerivVar(x, index),
                      DerivVar(y, index+1),
                      DerivVar(z, index+2))
