"""
Core functions used by unumpy and some of its submodules.

(c) 2010-2013 by Eric O. LEBIGOT (EOL).
"""

# The functions found in this module cannot be defined in unumpy or
# its submodule: this creates import loops, when unumpy explicitly
# imports one of the submodules in order to make it available to the
# user.

from __future__ import division

# Standard modules:
import sys

# 3rd-party modules:
import numpy
from numpy.core import numeric

# Local modules:
import uncertainties
from uncertainties import umath

from uncertainties import __author__

__all__ = [
    # Factory functions:
    'uarray', 'umatrix',

    # Utilities:
    'nominal_values', 'std_devs',

    # Classes:
    'matrix'
    ]

###############################################################################
# Utilities:

# nominal_values() and std_devs() are defined as functions (instead of
# as additional methods of the unumpy.matrix class) because the user
# might well directly build arrays of numbers with uncertainties
# without going through the factory functions found in this module
# (uarray() and umatrix()).  Thus,
# numpy.array([uncertainties.ufloat((1, 0.1))]) would not
# have a nominal_values() method.  Adding such a method to, say,
# unumpy.matrix, would break the symmetry between NumPy arrays and
# matrices (no nominal_values() method), and objects defined in this
# module.

# ! Warning: the __doc__ is set, but help(nominal_values) does not
# display it, but instead displays the documentation for the type of
# nominal_values (i.e. the documentation of its class):

to_nominal_values = numpy.vectorize(
    uncertainties.nominal_value,
    otypes=[float],  # Because vectorize() has side effects (dtype setting)
    doc=("Applies uncertainties.nominal_value to the elements of"
         " a NumPy (or unumpy) array (this includes matrices)."))

to_std_devs = numpy.vectorize(
    uncertainties.std_dev,
    otypes=[float],  # Because vectorize() has side effects (dtype setting)
    doc=("Returns the standard deviation of the numbers with uncertainties"
         " contained in a NumPy array, or zero for other objects."))

def unumpy_to_numpy_matrix(arr):
    """
    If arr in a unumpy.matrix, it is converted to a numpy.matrix.
    Otherwise, it is returned unchanged.
    """

    return arr.view(numpy.matrix) if isinstance(arr, matrix) else arr

def nominal_values(arr):
    """
    Returns the nominal values of the numbers in NumPy array arr.

    Elements that are not uncertainties.AffineScalarFunc are passed
    through untouched (because a numpy.array can contain numbers with
    uncertainties and pure floats simultaneously).

    If arr is of type unumpy.matrix, the returned array is a
    numpy.matrix, because the resulting matrix does not contain
    numbers with uncertainties.
    """

    return unumpy_to_numpy_matrix(to_nominal_values(arr))

def std_devs(arr):
    """
    Returns the standard deviations of the numbers in NumPy array arr.

    Elements that are not uncertainties.AffineScalarFunc are given a
    zero uncertainty ((because a numpy.array can contain numbers with
    uncertainties and pure floats simultaneously)..

    If arr is of type unumpy.matrix, the returned array is a
    numpy.matrix, because the resulting matrix does not contain
    numbers with uncertainties.
    """

    return unumpy_to_numpy_matrix(to_std_devs(arr))

###############################################################################

def derivative(u, var):
    """
    Returns the derivative of u along var, if u is an
    uncertainties.AffineScalarFunc instance, and if var is one of the
    variables on which it depends.  Otherwise, return 0.
    """
    if isinstance(u, uncertainties.AffineScalarFunc):
        try:
            return u.derivatives[var]
        except KeyError:
            return 0.
    else:
        return 0.

def wrap_array_func(func):
    """
    Returns a version of the function func() that works even when
    func() is given a NumPy array that contains numbers with
    uncertainties.

    func() is supposed to return a NumPy array.

    This wrapper is similar to uncertainties.wrap(), except that it
    handles an array argument instead of float arguments.

    func -- version that takes and returns a single NumPy array.
    """

    @uncertainties.set_doc("""\
    Version of %s(...) that works even when its first argument is a NumPy
    array that contains numbers with uncertainties.

    Warning: elements of the first argument array that are not
    AffineScalarFunc objects must not depend on uncertainties.Variable
    objects in any way.  Otherwise, the dependence of the result in
    uncertainties.Variable objects will be incorrect.

    Original documentation:
    %s""" % (func.__name__, func.__doc__))
    def wrapped_func(arr, *args):
        # Nominal value:
        arr_nominal_value = nominal_values(arr)
        func_nominal_value = func(arr_nominal_value, *args)

        # The algorithm consists in numerically calculating the derivatives
        # of func:

        # Variables on which the array depends are collected:
        variables = set()
        for element in arr.flat:
            # floats, etc. might be present
            if isinstance(element, uncertainties.AffineScalarFunc):
                variables |= set(element.derivatives.iterkeys())

        # If the matrix has no variables, then the function value can be
        # directly returned:
        if not variables:
            return func_nominal_value

        # Calculation of the derivatives of each element with respect
        # to the variables.  Each element must be independent of the
        # others.  The derivatives have the same shape as the output
        # array (which might differ from the shape of the input array,
        # in the case of the pseudo-inverse).
        derivatives = numpy.vectorize(lambda _: {})(func_nominal_value)
        for var in variables:

            # A basic assumption of this package is that the user
            # guarantees that uncertainties cover a zone where
            # evaluated functions are linear enough.  Thus, numerical
            # estimates of the derivative should be good over the
            # standard deviation interval.  This is true for the
            # common case of a non-zero standard deviation of var.  If
            # the standard deviation of var is zero, then var has no
            # impact on the uncertainty of the function func being
            # calculated: an incorrect derivative has no impact.  One
            # scenario can give incorrect results, however, but it
            # should be extremely uncommon: the user defines a
            # variable x with 0 standard deviation, sets y = func(x)
            # through this routine, changes the standard deviation of
            # x, and prints y; in this case, the uncertainty on y
            # might be incorrect, because this program had no idea of
            # the scale on which func() is linear, when it calculated
            # the numerical derivative.

            # The standard deviation might be numerically too small
            # for the evaluation of the derivative, though: we set the
            # minimum variable shift.

            shift_var = max(var._std_dev/1e5, 1e-8*abs(var._nominal_value))
            # An exceptional case is that of var being exactly zero.
            # In this case, an arbitrary shift is used for the
            # numerical calculation of the derivative.  The resulting
            # derivative value might be quite incorrect, but this does
            # not matter as long as the uncertainty of var remains 0,
            # since it is, in this case, a constant.
            if not shift_var:
                shift_var = 1e-8

            # Shift of all the elements of arr when var changes by shift_var:
            shift_arr = array_derivative(arr, var)*shift_var

            # Origin value of array arr when var is shifted by shift_var:
            shifted_arr_values = arr_nominal_value + shift_arr
            func_shifted = func(shifted_arr_values, *args)
            numerical_deriv = (func_shifted-func_nominal_value)/shift_var

            # Update of the list of variables and associated
            # derivatives, for each element:
            for (derivative_dict, derivative_value) in (
                zip(derivatives.flat, numerical_deriv.flat)):

                if derivative_value:
                    derivative_dict[var] = derivative_value

        # numbers with uncertainties are build from the result:
        return numpy.vectorize(uncertainties.AffineScalarFunc)(
            func_nominal_value, derivatives)

    # It is easier to work with wrapped_func, which represents a
    # wrapped version of 'func', when it bears the same name as
    # 'func' (the name is used by repr(wrapped_func)).
    wrapped_func.__name__ = func.__name__

    return wrapped_func

###############################################################################
# Arrays

# Vectorized creation of an array of variables:

# ! Looking up uncertainties.Variable beforehand through '_Variable =
# uncertainties.Variable' does not result in a significant speed up:

_uarray = numpy.vectorize(lambda v, s: uncertainties.Variable(v, s),
                          otypes=[object])

def uarray((values, std_devs)):
    """
    Returns a NumPy array of numbers with uncertainties
    initialized with the given nominal values and standard
    deviations.

    values, std_devs -- valid arguments for numpy.array, with
    identical shapes (list of numbers, list of lists, numpy.ndarray,
    etc.).
    """

    return _uarray(values, std_devs)

###############################################################################

def array_derivative(array_like, var):
    """
    Returns the derivative of the given array with respect to the
    given variable.

    The returned derivative is a Numpy ndarray of the same shape as
    array_like, that contains floats.

    array_like -- array-like object (list, etc.)  that contains
    scalars or numbers with uncertainties.

    var -- Variable object.
    """
    return numpy.vectorize(lambda u: derivative(u, var),
                           # The type is set because an
                           # integer derivative should not
                           # set the output type of the
                           # array:
                           otypes=[float])(array_like)

def func_with_deriv_to_uncert_func(func_with_derivatives):
    """
    Returns a function that can be applied to array-like objects that
    contain numbers with uncertainties (lists, lists of lists, Numpy
    arrays, etc.).

    func_with_derivatives -- defines a function that takes array-like
    objects containing scalars and returns an array.  Both the value
    and the derivatives of this function with respect to multiple
    scalar parameters are calculated by func_with_derivatives().

    func_with_derivatives(arr, input_type, derivatives, *args) returns
    an iterator.  The first element is the value of the function at
    point 'arr' (with the correct type).  The following elements are
    arrays that represent the derivative of the function for each
    derivative array from the iterator 'derivatives'.

      func_with_derivatives takes the following arguments:

      arr -- Numpy ndarray of scalars where the function must be
      evaluated.

      input_type -- type of the input array-like object.  This type is
      used for determining the type that the function should return.

      derivatives -- iterator that returns the derivatives of the
      argument of the function with respect to multiple scalar
      variables.  func_with_derivatives() returns the derivatives of
      the defined function with respect to these variables.

      args -- additional arguments that define the result (example:
      for the pseudo-inverse numpy.linalg.pinv: numerical cutoff).

    Examples of func_with_derivatives: inv_with_derivatives().
    """

    def wrapped_func(array_like, *args):
        """
        array_like -- array-like object that contains numbers with
        uncertainties (list, Numpy ndarray or matrix, etc.).

        args -- additional arguments that are passed directly to
        func_with_derivatives.
        """

        # So that .flat works even if array_like is a list.  Later
        # useful for faster code:
        array_version = numpy.asarray(array_like)

        # Variables on which the array depends are collected:
        variables = set()
        for element in array_version.flat:
            # floats, etc. might be present
            if isinstance(element, uncertainties.AffineScalarFunc):
                variables |= set(element.derivatives.iterkeys())

        array_nominal = nominal_values(array_version)
        # Function value, and derivatives at array_nominal (the
        # derivatives are with respect to the variables contained in
        # array_like):
        func_and_derivs = func_with_derivatives(
            array_nominal,
            type(array_like),
            (array_derivative(array_version, var) for var in variables),
            *args)

        func_nominal_value = func_and_derivs.next()

        if not variables:
            return func_nominal_value

        # The result is built progressively, with the contribution of
        # each variable added in turn:

        # Calculation of the derivatives of the result with respect to
        # the variables.
        derivatives = numpy.array(
            [{} for _ in xrange(func_nominal_value.size)], dtype=object)
        derivatives.resize(func_nominal_value.shape)

        # Memory-efficient approach.  A memory-hungry approach would
        # be to calculate the matrix derivatives will respect to all
        # variables and then combine them into a matrix of
        # AffineScalarFunc objects.  The approach followed here is to
        # progressively build the matrix of derivatives, by
        # progressively adding the derivatives with respect to
        # successive variables.
        for (var, deriv_wrt_var) in zip(variables, func_and_derivs):

            # Update of the list of variables and associated
            # derivatives, for each element:
            for (derivative_dict, derivative_value) in zip(
                derivatives.flat, deriv_wrt_var.flat):
                if derivative_value:
                    derivative_dict[var] = derivative_value

        # An array of numbers with uncertainties are built from the
        # result:
        result = numpy.vectorize(uncertainties.AffineScalarFunc)(
            func_nominal_value, derivatives)

        # Numpy matrices that contain numbers with uncertainties are
        # better as unumpy matrices:
        if isinstance(result, numpy.matrix):
            result = result.view(matrix)

        return result

    return wrapped_func

########## Matrix inverse

def inv_with_derivatives(arr, input_type, derivatives):
    """
    Defines the matrix inverse and its derivatives.

    See the definition of func_with_deriv_to_uncert_func() for its
    detailed semantics.
    """

    inverse = numpy.linalg.inv(arr)
    # The inverse of a numpy.matrix is a numpy.matrix.  It is assumed
    # that numpy.linalg.inv is such that other types yield
    # numpy.ndarrays:
    if issubclass(input_type, numpy.matrix):
        inverse = inverse.view(numpy.matrix)
    yield inverse

    # It is mathematically convenient to work with matrices:
    inverse_mat = numpy.asmatrix(inverse)

    # Successive derivatives of the inverse:
    for derivative in derivatives:
        derivative_mat = numpy.asmatrix(derivative)
        yield -inverse_mat * derivative_mat * inverse_mat

_inv = func_with_deriv_to_uncert_func(inv_with_derivatives)
_inv.__doc__ = """\
    Version of numpy.linalg.inv that works with array-like objects
    that contain numbers with uncertainties.

    The result is a unumpy.matrix if numpy.linalg.pinv would return a
    matrix for the array of nominal values.

    Analytical formulas are used.

    Original documentation:
    %s
    """ % numpy.linalg.inv.__doc__

########## Matrix pseudo-inverse

def pinv_with_derivatives(arr, input_type, derivatives, rcond):
    """
    Defines the matrix pseudo-inverse and its derivatives.

    Works with real or complex matrices.

    See the definition of func_with_deriv_to_uncert_func() for its
    detailed semantics.
    """

    inverse = numpy.linalg.pinv(arr, rcond)
    # The pseudo-inverse of a numpy.matrix is a numpy.matrix.  It is
    # assumed that numpy.linalg.pinv is such that other types yield
    # numpy.ndarrays:
    if issubclass(input_type, numpy.matrix):
        inverse = inverse.view(numpy.matrix)
    yield inverse

    # It is mathematically convenient to work with matrices:
    inverse_mat = numpy.asmatrix(inverse)

    # Formula (4.12) from The Differentiation of Pseudo-Inverses and
    # Nonlinear Least Squares Problems Whose Variables
    # Separate. Author(s): G. H. Golub and V. Pereyra. Source: SIAM
    # Journal on Numerical Analysis, Vol. 10, No. 2 (Apr., 1973),
    # pp. 413-432

    # See also
    # http://mathoverflow.net/questions/25778/analytical-formula-for-numerical-derivative-of-the-matrix-pseudo-inverse

    # Shortcuts.  All the following factors should be numpy.matrix objects:
    PA = arr*inverse_mat
    AP = inverse_mat*arr
    factor21 = inverse_mat*inverse_mat.H
    factor22 = numpy.eye(arr.shape[0])-PA
    factor31 = numpy.eye(arr.shape[1])-AP
    factor32 = inverse_mat.H*inverse_mat

    # Successive derivatives of the inverse:
    for derivative in derivatives:
        derivative_mat = numpy.asmatrix(derivative)
        term1 = -inverse_mat*derivative_mat*inverse_mat
        derivative_mat_H = derivative_mat.H
        term2 = factor21*derivative_mat_H*factor22
        term3 = factor31*derivative_mat_H*factor32
        yield term1+term2+term3

# Default rcond argument for the generalization of numpy.linalg.pinv:
try:
    # Python 2.6+:
    _pinv_default = numpy.linalg.pinv.__defaults__[0]
except AttributeError:
    _pinv_default = 1e-15

_pinv_with_uncert = func_with_deriv_to_uncert_func(pinv_with_derivatives)

@uncertainties.set_doc("""
    Version of numpy.linalg.pinv that works with array-like objects
    that contain numbers with uncertainties.

    The result is a unumpy.matrix if numpy.linalg.pinv would return a
    matrix for the array of nominal values.

    Analytical formulas are used.

    Original documentation:
    %s
    """ % numpy.linalg.pinv.__doc__)
def _pinv(array_like, rcond=_pinv_default):
    return _pinv_with_uncert(array_like, rcond)

########## Matrix class

class matrix(numpy.matrix):
    # The name of this class is the same as NumPy's, which is why it
    # does not follow PEP 8.
    """
    Class equivalent to numpy.matrix, but that behaves better when the
    matrix contains numbers with uncertainties.
    """

    def __rmul__(self, other):
        # ! NumPy's matrix __rmul__ uses an apparently a restrictive
        # dot() function that cannot handle the multiplication of a
        # scalar and of a matrix containing objects (when the
        # arguments are given in this order).  We go around this
        # limitation:
        if numeric.isscalar(other):
            return numeric.dot(self, other)
        else:
            return numeric.dot(other, self)  # The order is important

    # The NumPy doc for getI is empty:
    # @uncertainties.set_doc(numpy.matrix.getI.__doc__)
    def getI(self):
        "Matrix inverse of pseudo-inverse"

        # numpy.matrix.getI is OK too, but the rest of the code assumes that
        # numpy.matrix.I is a property object anyway:

        M, N = self.shape
        if M == N:
            func = _inv
        else:
            func = _pinv
        return func(self)


    # ! In Python >= 2.6, this could be simplified as:
    # I = numpy.matrix.I.getter(__matrix_inverse)
    I = property(getI, numpy.matrix.I.fset, numpy.matrix.I.fdel,
                 numpy.matrix.I.__doc__)

    @property
    def nominal_values(self):
        """
        Nominal value of all the elements of the matrix.
        """
        return nominal_values(self)

    std_devs = std_devs

def umatrix(*args):
    """
    Constructs a matrix that contains numbers with uncertainties.

    The input data is the same as for uarray(...): a tuple with the
    nominal values, and the standard deviations.

    The returned matrix can be inverted, thanks to the fact that it is
    a unumpy.matrix object instead of a numpy.matrix one.
    """

    return uarray(*args).view(matrix)

###############################################################################

def define_vectorized_funcs():
    """
    Defines vectorized versions of functions from uncertainties.umath.

    Some functions have their name translated, so as to follow NumPy's
    convention (example: math.acos -> numpy.arccos).
    """

    this_module = sys.modules[__name__]
    # NumPy does not always use the same function names as the math
    # module:
    func_name_translations = dict(
        (f_name, 'arc'+f_name[1:])
        for f_name in ['acos', 'acosh', 'asin', 'atan', 'atan2', 'atanh'])

    new_func_names = [func_name_translations.get(function_name, function_name)
                      for function_name in umath.many_scalars_to_scalar_funcs]

    for (function_name, unumpy_name) in zip(
        umath.many_scalars_to_scalar_funcs, new_func_names):

        # ! The newly defined functions (uncertainties.unumpy.cos, etc.)
        # do not behave exactly like their NumPy equivalent (numpy.cos,
        # etc.): cos(0) gives an array() and not a
        # numpy.float... (equality tests succeed, though).
        func = getattr(umath, function_name)
        setattr(
            this_module, unumpy_name,
            numpy.vectorize(func,
                            # If by any chance a function returns,
                            # in a particular case, an integer,
                            # side-effects in vectorize() would
                            # fix the resulting dtype to integer,
                            # which is not what is wanted:
                            otypes=[object],
                            doc="""\
Vectorized version of umath.%s.

Original documentation:
%s""" % (function_name, func.__doc__)))

        __all__.append(unumpy_name)

define_vectorized_funcs()
