"""
Provides the basic classes needed to specify statistical models.



namespace : dictionary
   mapping from names to data, used to associate data to a formula or term


"""
from collections import OrderedDict
from statsmodels.compat.python import (iterkeys, lrange, string_types,
                                       itervalues, range)
import copy
import numpy as np

__docformat__ = 'restructuredtext'

default_namespace = {}

class Term(object):
    """
    This class is very simple: it is just a named term in a model formula.

    It is also callable: by default it namespace[self.name], where namespace
    defaults to formula.default_namespace.
    When called in an instance of formula,
    the namespace used is that formula's namespace.

    Inheritance of the namespace under +,*,- operations:
    ----------------------------------------------------

    By default, the namespace is empty, which means it must be
    specified before evaluating the design matrix.

    When it is unambiguous, the namespaces of objects are derived from the
    context.

    Rules:
    ------

    i) "X * I", "X + I", "X**i": these inherit X's namespace
    ii) "F.main_effect()": this inherits the Factor F's namespace
    iii) "A-B": this inherits A's namespace
    iv) if A.namespace == B.namespace, then A+B inherits this namespace
    v) if A.namespace == B.namespace, then A*B inherits this namespace

    Equality of namespaces:
    -----------------------

    This is done by comparing the namespaces directly, if
    an exception is raised in the check of equality, they are
    assumed not to be equal.
    """

    def __pow__(self, power):
        """
        Raise the quantitative term's values to an integer power, i.e.
        polynomial.
        """

        try:
            power = float(power)
        except:
            raise ValueError('expecting a float')

        if power == int(power):
            name = '%s^%d' % (self.name, int(power))
        else:
            name = '%s^%0.2f' % (self.name, power)

        value = Quantitative(name, func=self, transform=lambda x: np.power(x, power))
        value.power = power
        value.namespace = self.namespace
        return value

    def __init__(self, name, func=None, termname=None):

        self.name = name
        self.__namespace = None
        if termname is None:
            self.termname = name
        else:
            self.termname = termname

        if not isinstance(self.termname, string_types):
            raise ValueError('expecting a string for termname')
        if func:
            self.func = func

    # Namespace in which self.name will be looked up in, if needed

    def _get_namespace(self):
        if isinstance(self.__namespace, np.ndarray):
            return self.__namespace
        else: return self.__namespace or default_namespace

    def _set_namespace(self, value):  self.__namespace = value
    def _del_namespace(self): del self.__namespace
    namespace = property(_get_namespace, _set_namespace, _del_namespace)

    def __str__(self):
        """
        '<term: %s>' % self.termname
        """
        return '<term: %s>' % self.termname

    def __add__(self, other):
        """
        Formula(self) + Formula(other)
        """
        fother = Formula(other, namespace=other.namespace)
        f = fother + self
        if _namespace_equal(fother.namespace, self.namespace):
            f.namespace = self.namespace
        return f

    def __mul__(self, other):
        """
        Formula(self) * Formula(other)
        """

        if isinstance(other, Term) and other.name == 'intercept':
            f = Formula(self, namespace=self.namespace)
        elif self.name == 'intercept':
            f = Formula(other, namespace=other.namespace)
        else:
            other = Formula(other, namespace=other.namespace)
            f = other * self
        if _namespace_equal(other.namespace, self.namespace):
            f.namespace = self.namespace
        return f

    def names(self):
        """
        Return the names of the columns in design associated to the terms,
        i.e. len(self.names()) = self().shape[0].
        """
        if isinstance(self.name, string_types):
            return [self.name]
        else:
            return list(self.name)

    def __call__(self, *args, **kw):
        """
        Return the columns associated to self in a design matrix.
        If the term has no 'func' attribute, it returns
        ``self.namespace[self.termname]``
        else, it returns
        ``self.func(*args, **kw)``
        """

        if not hasattr(self, 'func'):
            val = self.namespace[self.termname]
        else:
            val = self.func
        if callable(val):
            if isinstance(val, (Term, Formula)):
                val = copy.copy(val)
                val.namespace = self.namespace
            val = val(*args, **kw)

        val = np.asarray(val)
        return np.squeeze(val)

class Factor(Term):
    """A categorical factor."""

    def __init__(self, termname, keys, ordinal=False):
        """
        Factor is initialized with keys, representing all valid
        levels of the factor.

        If ordinal is False, keys can have repeats: set(keys) is what is
        used.

        If ordinal is True, the order is taken from the keys, and
        there should be no repeats.
        """

        if not ordinal:
            self.keys = list(set(keys))
            self.keys.sort()
        else:
            self.keys = keys
            if len(set(keys)) != len(list(keys)):
                raise ValueError('keys for ordinal Factor should be unique, in increasing order')
        self._name = termname
        self.termname = termname
        self.ordinal = ordinal

        if self.ordinal:
            name = self.termname
        else:
            name = ['(%s==%s)' % (self.termname, str(key)) for key in self.keys]

        Term.__init__(self, name, termname=self.termname, func=self.get_columns)

    def get_columns(self, *args, **kw):
        """
        Calling function for factor instance.
        """

        v = self.namespace[self._name]
        while True:
            if callable(v):
                if isinstance(v, (Term, Formula)):
                    v = copy.copy(v)
                    v.namespace = self.namespace
                v = v(*args, **kw)
            else: break

        n = len(v)

        if self.ordinal:
            col = [float(self.keys.index(v[i])) for i in range(n)]
            return np.array(col)

        else:
            value = []
            for key in self.keys:
                col = [float((v[i] == key)) for i in range(n)]
                value.append(col)
            return np.array(value)

    def values(self, *args, **kw):
        """
        Return the keys of the factor, rather than the columns of the design
        matrix.
        """

        del(self.func)
        val = self(*args, **kw)
        self.func = self.get_columns
        return val

    def verify(self, values):
        """
        Verify that all values correspond to valid keys in self.
        """
        s = set(values)
        if not s.issubset(self.keys):
            raise ValueError('unknown keys in values')

    def __add__(self, other):
        """
        Formula(self) + Formula(other)

        When adding \'intercept\' to a factor, this just returns

        Formula(self, namespace=self.namespace)

        """

        if isinstance(other, Term) and other.name == 'intercept':
            return Formula(self, namespace=self.namespace)
        else:
            return Term.__add__(self, other)

    def main_effect(self, reference=None):
        """
        Return the 'main effect' columns of a factor, choosing
        an optional reference key.

        The reference key can be one of the keys of the Factor,
        or an integer, representing which column to remove.
        It defaults to 0.

        """

        names = self.names()

        if reference is None:
            reference = 0
        else:
            try:
                reference = self.keys.index(reference)
            except ValueError:
                reference = int(reference)

        def maineffect_func(value, reference=reference):
            rvalue = []
            keep = lrange(value.shape[0])
            keep.pop(reference)
            for i in range(len(keep)):
                rvalue.append(value[keep[i]] - value[reference])
            return np.array(rvalue)

        keep = lrange(len(self.names()))
        keep.pop(reference)
        __names = self.names()
        _names = ['%s-%s' % (__names[keep[i]], __names[reference]) for i in range(len(keep))]
        value = Quantitative(_names, func=self,
                     termname='%s:maineffect' % self.termname,
                     transform=maineffect_func)
        value.namespace = self.namespace
        return value

    def __getitem__(self, key):
        """
        Retrieve the column corresponding to key in a Formula.

        Parameters
        ----------
        key : Factor key
            one of the Factor's keys

        Returns
        -------
        ndarray corresponding to key, when evaluated in current namespace
        """
        if not self.ordinal:
            i = self.names().index('(%s==%s)' % (self.termname, str(key)))
            return self()[i]
        else:
            v = self.namespace[self._name]
            return np.array([(vv == key) for vv in v]).astype(np.float)


class Quantitative(Term):
    """
    A subclass of term that can be used to apply point transformations
    of another term, i.e. to take powers:

    >>> import numpy as np
    >>> from nipy.fixes.scipy.stats.models import formula
    >>> X = np.linspace(0,10,101)
    >>> x = formula.Term('X')
    >>> x.namespace={'X':X}
    >>> x2 = x**2
    >>> print np.allclose(x()**2, x2())
    True
    >>> x3 = formula.Quantitative('x2', func=x, transform=lambda x: x**2)
    >>> x3.namespace = x.namespace
    >>> print np.allclose(x()**2, x3())
    True

    """

    def __init__(self, name, func=None, termname=None, transform=lambda x: x):
        self.transform = transform
        Term.__init__(self, name, func=func, termname=termname)

    def __call__(self, *args, **kw):
        """
        A quantitative is just like term, except there is an additional
        transformation: self.transform.

        """
        return self.transform(Term.__call__(self, *args, **kw))

class Formula(object):
    """
    A formula object for manipulating design matrices in regression models,
    essentially consisting of a list of term instances.

    The object supports addition and multiplication which correspond
    to concatenation and pairwise multiplication, respectively,
    of the columns of the two formulas.

    """

    def _get_namespace(self):
        if isinstance(self.__namespace, np.ndarray):
            return self.__namespace
        else: return self.__namespace or default_namespace

    def _set_namespace(self, value):  self.__namespace = value
    def _del_namespace(self): del self.__namespace
    namespace = property(_get_namespace, _set_namespace, _del_namespace)

    def _terms_changed(self):
        self._names = self.names()
        self._termnames = self.termnames()

    def __init__(self, termlist, namespace=default_namespace):
        """
        Create a formula from either:
         i. a `formula` object
         ii. a sequence of `term` instances
         iii. one `term`
        """


        self.__namespace = namespace
        if isinstance(termlist, Formula):
            self.terms = copy.copy(list(termlist.terms))
        elif isinstance(termlist, list):
            self.terms = termlist
        elif isinstance(termlist, Term):
            self.terms = [termlist]
        else:
            raise ValueError

        self._terms_changed()

    def __str__(self):
        """
        String representation of list of termnames of a formula.
        """
        value = []
        for term in self.terms:
            value += [term.termname]
        return '<formula: %s>' % ' + '.join(value)

    def __call__(self, *args, **kw):

        """
        Create (transpose) of the design matrix of the formula within
        namespace. Extra arguments are passed to each term instance. If
        the formula just contains an intercept, then the keyword
        argument 'nrow' indicates the number of rows (observations).
        """

        if 'namespace' in kw:
            namespace = kw['namespace']
        else:
            namespace = self.namespace


        allvals = []
        intercept = False
        iindex = 0
        for t in self.terms:
            t = copy.copy(t)
            t.namespace = namespace
            val = t(*args, **kw)

            isintercept = False
            if hasattr(t, "termname"):
                if t.termname == 'intercept':
                    intercept = True
                    isintercept = True
                    interceptindex = iindex
                    allvals.append(None)

            if val.ndim == 1 and not isintercept:
                val.shape = (1, val.shape[0])
                allvals.append(val)
            elif not isintercept:
                allvals.append(val)
            iindex += 1

        if not intercept:
            try:
                allvals = np.concatenate(allvals)
            except:
                pass
        else:
            nrow = kw.get('nrow', -1)
            if allvals != []:
                if interceptindex > 0:
                    n = allvals[0].shape[1]
                else:
                    n = allvals[1].shape[1]
                allvals[interceptindex] = np.ones((1,n), np.float64)
                allvals = np.concatenate(allvals)
            elif nrow <= 1:
                raise ValueError('with only intercept in formula, keyword \'nrow\' argument needed')
            else:
                allvals = I(nrow=nrow)
                allvals.shape = (1,) + allvals.shape
        return np.squeeze(allvals)

    def hasterm(self, query_term):
        """
        Determine whether a given term is in a formula.
        """

        if not isinstance(query_term, Formula):
            if isinstance(query_term, string_types):
                try:
                    query = self[query_term]
                    return query.termname in self.termnames()
                except:
                    return False
            elif isinstance(query_term, Term):
                return query_term.termname in self.termnames()
        elif len(query_term.terms) == 1:
            query_term = query_term.terms[0]
            return query_term.termname in self.termnames()
        else:
            raise ValueError('more than one term passed to hasterm')

    def __getitem__(self, name):
        t = self.termnames()
        if name in t:
            return self.terms[t.index(name)]
        else:
            raise KeyError('formula has no such term: %s' % repr(name))

    def termcolumns(self, query_term, dict=False):
        """
        Return a list of the indices of all columns associated
        to a given term.
        """

        if self.hasterm(query_term):
            names = query_term.names()
            value = OrderedDict()
            for name in names:
                value[name] = self._names.index(name)
        else:
            raise ValueError('term not in formula')
        if dict:
            return value
        else:
            return list(itervalues(value))

    def names(self):
        """
        Return a list of the names in the formula. The order of the
        names corresponds to the order of the columns when self
        is evaluated.
        """

        allnames = []
        for term in self.terms:
            allnames += term.names()
        return allnames

    def termnames(self):
        """
        Return a list of the term names in the formula. These
        are the names of each term instance in self.
        """

        names = []
        for term in self.terms:
            names += [term.termname]
        return names

    def design(self, *args, **kw):
        """
        ``transpose(self(*args, **kw))``
        """
        return self(*args, **kw).T

    def __mul__(self, other, nested=False):
        """
        This returns a formula whose columns are the pairwise
        product of the columns of self and other.

        TO DO: check for nesting relationship. Should not be too difficult.
        """

        other = Formula(other)

        selftermnames = self.termnames()
        othertermnames = other.termnames()

        n_term_names = len(selftermnames)
        J = len(othertermnames)

        terms = []
        termnames = []

        for i in range(n_term_names):
            for j in range(J):
                termname = '%s*%s' % (str(selftermnames[i]), str(othertermnames[j]))
                pieces = sorted(termname.split('*'))
                termname = '*'.join(pieces)
                termnames.append(termname)

                selfnames = self.terms[i].names()
                othernames = other.terms[j].names()

                if self.terms[i].name == 'intercept':
                    _term = other.terms[j]
                    _term.namespace = other.namespace
                elif other.terms[j].name == 'intercept':
                    _term = self.terms[i]
                    _term.namespace = self.namespace
                else:
                    names = []

                    d1 = len(selfnames)
                    d2 = len(othernames)

                    for r in range(d1):
                        for s in range(d2):
                            name = '%s*%s' % (str(selfnames[r]), str(othernames[s]))
                            pieces = sorted(name.split('*'))
                            name = '*'.join(pieces)
                            names.append(name)

                    def product_func(value, d1=d1, d2=d2):

                        out = []
                        for r in range(d1):
                            for s in range(d2):
                                out.append(value[r] * value[d1+s])
                        return np.array(out)

                    cself = copy.copy(self.terms[i])
                    cother = copy.copy(other.terms[j])
                    sumterms = cself + cother
                    sumterms.terms = [cself, cother] # enforce the order we want

                    _term = Quantitative(names, func=sumterms,
                                         termname=termname,
                                         transform=product_func)

                    if _namespace_equal(self.namespace, other.namespace):
                        _term.namespace = self.namespace

                terms.append(_term)

        return Formula(terms)

    def __add__(self, other):

        """
        Return a formula whose columns are the
        concatenation of the columns of self and other.

        terms in the formula are sorted alphabetically.
        """

        def _delist(x):
            if isinstance(x, list):
                return x[0]
            else:
                return x

        other = Formula(other)
        terms = self.terms + other.terms
        pieces = sorted([(_delist(term.name), term) for term in terms], key=lambda x: x[0])
        terms = [piece[1] for piece in pieces]
        f = Formula(terms)
        if _namespace_equal(self.namespace, other.namespace):
            f.namespace = self.namespace
        return f

    def __sub__(self, other):

        """
        Return a formula with all terms in other removed from self.
        If other contains term instances not in formula, this
        function does not raise an exception.
        """

        other = Formula(other)
        terms = copy.copy(self.terms)

        for term in other.terms:
            for i in range(len(terms)):
                if terms[i].termname == term.termname:
                    terms.pop(i)
                    break
        f = Formula(terms)
        f.namespace = self.namespace
        return f

def isnested(A, B, namespace=None):
    """
    Is factor B nested within factor A or vice versa: a very crude test
    which depends on the namespace.

    If they are nested, returns (True, F) where F is the finest
    level of the relationship. Otherwise, returns (False, None)

    """

    if namespace is not None:
        A = copy.copy(A)
        A.namespace = namespace
        B = copy.copy(B)
        B.namespace = namespace

    a = A(values=True)[0]
    b = B(values=True)[0]

    if len(a) != len(b):
        raise ValueError('A() and B() should be sequences of the same length')

    nA = len(set(a))
    nB = len(set(b))
    n = max(nA, nB)

    AB = [(a[i],b[i]) for i in range(len(a))]
    nAB = len(set(AB))

    if nAB == n:
        if nA > nB:
            F = A
        else:
            F = B
        return (True, F)
    else:
        return (False, None)

def _intercept_fn(nrow=1, **extra):
    return np.ones((1,nrow))

I = Term('intercept', func=_intercept_fn)  # noqa:E741
I.__doc__ = """
Intercept term in a formula. If intercept is the
only term in the formula, then a keyword argument
\'nrow\' is needed.

>>> from nipy.fixes.scipy.stats.models.formula import Formula, I
>>> I()
array(1.0)
>>> I(nrow=5)
array([ 1.,  1.,  1.,  1.,  1.])
>>> f=Formula(I)
>>> f(nrow=5)
array([1, 1, 1, 1, 1])

"""

def interactions(terms, order=[1,2]):
    """
    Output all pairwise interactions of given order of a
    sequence of terms.

    The argument order is a sequence specifying which order
    of interactions should be generated -- the default
    creates main effects and two-way interactions. If order
    is an integer, it is changed to range(1,order+1), so
    order=3 is equivalent to order=[1,2,3], generating
    all one, two and three-way interactions.

    If any entry of order is greater than len(terms), it is
    effectively treated as len(terms).

    >>> print interactions([Term(l) for l in ['a', 'b', 'c']])
    <formula: a*b + a*c + b*c + a + b + c>
    >>>
    >>> print interactions([Term(l) for l in ['a', 'b', 'c']], order=list(range(5)))
    <formula: a*b + a*b*c + a*c + b*c + a + b + c>
    >>>

    """
    n_terms = len(terms)

    values = {}

    if np.asarray(order).shape == ():
        order = lrange(1, int(order)+1)

    # First order

    for o in order:
        indices = np.indices((n_terms,)*(o))
        indices.shape = (indices.shape[0], np.product(indices.shape[1:]))
        for m in range(indices.shape[1]):

            # only keep combinations that have unique entries

            if (np.unique(indices[:,m]).shape == indices[:,m].shape and
                    np.alltrue(np.equal(np.sort(indices[:,m]), indices[:,m]))):
                ll = [terms[j] for j in indices[:,m]]
                v = ll[0]
                for ii in range(len(ll)-1):
                    v *= ll[ii+1]
                values[tuple(indices[:,m])] = v

    key = list(iterkeys(values))[0]
    value = values[key]
    del(values[key])

    for v in itervalues(values):
        value += v
    return value

def _namespace_equal(space1, space2):
    return space1 is space2
